Dynamically composable object tracker configuration for intelligent video analytics systems

Apparatuses, systems, and techniques for managing lost objects in an intelligent video analytics system. A first set of application modules is executed for an object tracking application configured to track, based on images depicting an environment, a state of objects included in the environment. The first set of application modules is associated with a first object tracker type. A request is received to configure the object tracking application to execute a second set of application modules associated with a second object tracker type. The second set of application modules includes one or more application modules that are different from application modules of the first set of application modules. The object tracking application is configured to execute the second set of application modules in accordance with the request. The second set of application modules is executed for the object tracking application to track, based on the images depicting the environment, the state of the objects included in the environment.

TECHNICAL FIELD

At least one embodiment pertains to processing resources used to configuring an object tracker for an intelligent video analytics system. For example, at least one embodiment pertains to processors or computing systems used to enable an intelligent video analytics system to configure an object tracker based on configuration settings provided by a user of the intelligent video analytics system, according to various novel techniques described herein.

BACKGROUND

Efficient and effective object tracking is a critical task in video monitoring applications, such as video analytics, video surveillance, activity recognition, vehicle navigation, etc. Some systems may utilize one or more object detection models to detect objects included in images depicting an environment. Such systems may estimate a state (e.g., a position, a location, a size, a scale, a velocity, etc.) of the detected object within the environment relative to a camera that generated the images, relative to other objects included in the environment, etc. The system may track the detected object's (also referred to as a target) state in subsequent images depicting the environment and may provide information associated with the target's state to a user of the system (e.g., via a client device, etc.).

DETAILED DESCRIPTION

Multiple different types of object trackers have been developed to enable tracking of objects in an environment surveilled by a camera. Each type of object tracker offers its own benefits and limitations based on the type of system and/or resources available to a system that is implementing the object tracker, the type of environment and/or objects in the environment that are being surveilled, and/or the overall target accuracy and/or performance conditions imposed on the system. For example, one type of object tracker may be configured to track a detected object based on a metric such as the location or the Intersection-over-Union (IOU) of one or more bounding boxes (e.g., obtained from an output of an object detection model, etc.) corresponding to the detected object in images depicting an environment (referred to herein as a location-based tracker). A location-based tracker may not implement complex feature extraction or visual analysis techniques, and therefore may consume fewer computing resources (e.g., processor cycles, memory storage space, etc.) than other object tracker types that implement such techniques. However, because the location-based tracker may not implement such techniques, the location-based tracker may only be suitable for tracking objects with distinct shapes or sizes that are sparsely located within an environment.

In another example, another type of object tracker may be configured to track a detected object based on visual features extracted from a sequence of images depicting the object in an environment (e.g., using a visual feature extraction model). Such type of object tracker is referred to as a visual feature-based tracker or a feature-based tracker. A visual feature-based tracker may implement visual feature extraction techniques to track a detected object, and therefore may consume a larger amount of computing resources than a location-based tracker. However, the extracted visual features associated with a detected object may enable improved object tracking over the location-based tracker, and thus the visual feature-based tracker may be suitable for tracking a larger number of objects concentrated in an environment.

In yet another example, a discriminative correlation filter (DCF)-type object tracker may be configured to track a detected object based on a visual similarity between regions of a sequence of images that include the object (e.g., indicated by one or more bounding boxes corresponding to the object, a correlation response present in regions including the object, etc.) and/or are expected to include the object (e.g., based on an output of a state estimation function and/or a state prediction model). The DCF-type tracker may consume a larger amount of system resources than other object trackers (e.g., the location-based object tracker), which may result in a lower overall system efficiency and/or a higher overall system latency. However, the DCF-type tracker may be suitable to track a large number of objects concentrated in an environment, and/or objects that undergo occlusions (e.g., objects that seemingly merge or combine with other objects in the environment), which may not be possible using other object trackers.

A user of an intelligent video analytics system, such as (in an example, non-limiting embodiment) an object monitoring system may wish to configure different types of object trackers, depending on current system resource availability and/or conditions or characteristics of the environment and/or objects being surveilled. In one illustrative example, a small number of computing resources may be available to support object monitoring during an initialization of the system and accordingly, the user may configure a location-based tracker to run on the system. Over time, a larger number of computing resources may become available, and the user may wish to configure a visual feature-based tracker and/or a DCF-type tracker. Conventional systems configure object trackers based on distinct sets of program libraries that are particular to each object tracker type. For example, a conventional system configures a location-based tracker by enabling or otherwise activating a distinct set of program libraries that is particular to location-based trackers. To configure a visual feature-based tracker and/or a DCF-type tracker, the conventional system may deactivate or otherwise disable the entire set of location-based object libraries and enable a visual feature-based and/or a DCF-type tracker based on another distinct set of program libraries that is particular to these tracker types. Completely disabling one set of tracker libraries, enabling another set of tracker libraries, and configuring a system to execute an object tracker based on the newly-enabled set of tracker libraries may consume a significant amount of time and computing resources or may disrupt the processing of real-time data (e.g., especially in a streaming analytics system). In accordance with the previous example, visual feature-based and/or DCF-type tracker libraries may perform the same or similar functions as the location-based tracker (e.g., detected object monitoring functions, object data association and/or mapping functions, etc.), while also performing additional functions that are not performed by the location-based tracker (e.g., feature extraction and comparison functions, visual analysis functions, etc.). However, conventional systems do not enable users to leverage the functions performed by a first type of object tracker (e.g., a location-based tracker) to implement a second type of object tracker (e.g., a visual feature-based tracker, a DCF-type tracker, etc.) Further, conventional systems do not enable users to leverage the functions performed by existing types of object trackers to configure custom object trackers based on individual resource availability, distinctive conditions or characteristics of the environment and/or objects being surveilled, etc. associated with a respective intelligent video analytics system.

Embodiments of the present disclosure address the above and other deficiencies by providing a modular intelligent video analytics system (e.g., an object monitoring system) that enables a user to configure an object tracker application using application modules associated with multiple types of object trackers. A library, as provided herein, refers to a collection of resources (e.g., configuration files, executable programs, etc.) allocated to support one or more application modules of an object tracker application. An application module refers to a logical unit (e.g., a container, etc.) configured to execute a particular task associated with the object tracker application. The system of the present disclosure may maintain a set of common modules in different object tracker libraries that are each allocated to support distinct application modules associated with one or more object tracker types (e.g., a location-based tracker, a visual feature-based tracker, a DCF-type tracker, etc.). Each module maintained by the system may be configured to communicate and function with other modules that are activated for the object tracker, in view of configuration settings and/or files provided by a user of the system. Accordingly, a user is enabled to activate or deactivate one or more modules to configure a particular object tracker type (e.g., the location-based tracker, the visual feature-based tracker, a DCF-type tracker, a custom type tracker, etc.) in accordance with respective conditions associated with the intelligent video analytics system and/or the environment and/or objects being surveilled. As a respective module is activated or deactivated, the architecture of the intelligent video analytics system enables composition of an object tracker of the particular tracker type without disruption of the tracker module workflow.

Some application modules may be configured to execute tasks associated with each type of object tracker. For example, for a respective object tracked in an environment (also referred to herein as a target), a data association application module may be configured to obtain data (e.g., state data, etc.) associated with the target in view of a current image depicting the environment and in view of a prior image depicting the environment. The data association module may also be configured to generate a mapping between the object data obtained in view of the first image and the object data obtained in view of the second image. A target management application module may be configured to instantiate an object tracker for the target. The object tracker may monitor a state of the object based on mappings generated by the data association module and, in some embodiments, may be configured to provide an indication of the object state to a user of the system (e.g., via a client device connected to the system). Each type of object tracker supported by the intelligent video analytics system (e.g., location-based tracker, a visual feature-based tracker, a DCF-type tracker, a custom tracker, etc.) may be configured to implement the data association module and the target management module.

Additional modules may be configured to execute distinct tasks associated with particular types of object trackers. For example, a DCF-type tracker may implement one or more modules that enable localization of existing targets tracked by object trackers of the intelligent video analytics system. One or more modules (e.g., an object localization module, a data extraction module, etc.) of the DCF-type tracker may be configured to estimate a location of existing targets tracked by object trackers of the intelligent video analytics system based on a visual similarity between a region of a current image depicting an environment that includes a detected object (e.g., based on one or more bounding boxes obtained from an output of an object detection model, based on a correlation response determined for one or more regions of the image, etc.) and a region of the respective image that is expected to include the detected object (e.g., based on state data or predicted data associated with an existing target in view of one or more prior images). The one or more modules may extract data associated with the region of the current image that includes the detected object and compare the extracted data with corresponding data associated with existing targets (e.g., extracted from prior images depicting the environment). The object localization module may calculate the visual similarity metric value by determining a visual similarity between the detected object and an existing target based on the data extracted by the data extraction module and may calculate a similarity metric value based on the determined visual similarity. The object localization module may provide an indication of the image region that includes the detected object, the image region that is expected to include an existing target, and the calculated visual similarity metric value to the data association module, as described above. In another example, a visual feature-based tracker may also implement a data extraction application module, which may be configured to extract one or more visual features from an image depicting a detected object (i.e., using a feature extraction model) and calculate a feature similarity metric value based on the extracted features and features associated with an existing target extracted from prior image depicting the environment. The data extraction module may provide an indication of the image region that includes the detected object and the calculated feature similarity metric to the data association application module, as described above.

In some embodiments, a user of an intelligent video analytics system may request initialization of a particular type of object tracker (e.g., via a client device connected to the system). For example, the system can receive a request to configure a particular type of object tracker (e.g., in response to the user interacting with a GUI provided by the system, etc.). The system may identify one or more libraries that correspond to the particular type of object tracker and may initialize a first set of application modules based on the one or more identified libraries. Each of the initialized first set of application modules may be configured to communicate and function with the other initialized application modules (i.e., due to the unified architecture provided by embodiments of the present disclosure). The system may configure the object tracking application to execute the initialized first set of application modules (e.g., by updating a configuration file associated with the application to enable the first set of application modules) and may provide the configured object tracking application in accordance with the request.

In additional or alternative embodiments, after initializing an object tracking application to execute a first set of application modules associated with a first object tracker type (e.g., as described above), the system may receive a request to configure the application to execute a second set of application modules associated with a second tracker type. The second set of application modules may include one or more application modules that are different from the modules in the first set of application modules. For example, the first set of application modules may correspond to a DCF-type tracker and may include an object localization module and/or a data extraction module that is configured to extract a particular type of target data from images. The second set of application modules may correspond to a visual feature-based tracker and may include a data extraction module that is configured to extract a different type of target data (e.g., visual feature data) from images. In another example, the second set of application modules may correspond to both a DCF-type tracker and a visual feature-based tracker and therefore may include an object localization module and one or more data extraction modules configured to extract both types of target data from images. The system may configure the object tracking application to execute the second set of application modules (e.g., by updating a configuration file associated with the application), in accordance with the request, and may execute the second set of application modules to perform object tracking for detected objects and/or targets in a surveilled environment.

Aspects and embodiments of the present disclosure provide techniques to enable intelligent video analytics systems to leverage the functions performed by different types of object trackers (e.g., location-based trackers, visual feature-based trackers, DCF-type trackers, etc.) to dynamically configure each different type of object tracker. Embodiments of the present disclosure enable a user of an intelligent video analytics system to provide configuration settings and/or files for configuring a particular type of object tracker that can be used in accordance with system and/or environmental conditions without disabling a set of tracker libraries and re-enabling another set of tracker libraries. Accordingly, a user is enabled to switch between different object tracker types without significant disruption of the overall object tracking functionality provided by the intelligent video analytics system. Further, the modular architecture of the proposed system enables the system to leverage the functions performed by existing object tracker types to develop and implement new object tracker types.

System Architecture

FIG.1is a block diagram of an example system architecture100, according to at least one embodiment. The system architecture100(also referred to as “system” herein) may include a computing device102, an image source104, one or more client devices106, one or more data stores112, and/or server machines (e.g., server machines130-150), each connected to a network110. In implementations, network110may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a wired network (e.g., Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, and/or a combination thereof.

Computing device102may be a desktop computer, a laptop computer, a smartphone, a tablet computer, a server, or any suitable computing device capable of performing the techniques described herein. In some embodiments, computing device102may be a computing device of a cloud computing platform. For example, computing device102may be, or may be a component of, a server machine of a cloud computing platform. In such embodiments, computing device102may be coupled to one or more edge devices (not shown) via network110. An edge device refers to a computing device that enables communication between computing devices at the boundary of two networks. For example, an edge device may be connected to computing device102, data store112, server machine130, server machine140, and/or server machine150via network110, and may be connected to one or more endpoint devices (not shown) via another network. In such example, the edge device can enable communication between computing device102, data store112, server machine130, server machine140, and/or server machine150and the one or more endpoint devices. In other or similar embodiments, computing device102may be, or may be a component of, an edge device. For example, computing device102may facilitate communication between data store112, server machine130, server machine140, and/or server machine150, which are connected to computing device102via network110, and one or more endpoint devices that are connected to computing device102via another network.

In still other or similar embodiments, computing device102may be, or may be a component of, an endpoint device. For example, computing device102may be, or may be a component of, devices, such as, but not limited to: televisions, smart phones, cellular telephones, personal digital assistants (PDAs), portable media players, netbooks, laptop computers, electronic book readers, tablet computers, desktop computers, set-top boxes, gaming consoles, autonomous vehicles, surveillance devices, and the like. In such embodiments, computing device102may be connected to data store112, server machine130, server machine140and/or server machine150via network110. In other or similar embodiments, computing device102may be connected to an edge device (not shown) of system100via a network and the edge device of system100may be connected to data store112, server machine130, server machine140and/or server machine150via network110.

Image source104may be or may include one or sensors that are configured to generate data, such as visual data, audio data, etc., associated with an environment. The sensors can include an image sensor (e.g., a camera), a light detection and ranging (LIDAR) sensor, a radio detection and ranging (RADAR) sensor, sound navigation and ranging (SONAR) sensor, an ultrasonic sensor, a microphone, and other sensor types. In some embodiments, the data collected and/or generated by the sensors may represent a perception of the environment by the sensors. It should be noted that although some embodiments of the present disclosure are directed to image data (e.g., an image) generated by one or more sensors of image source104, embodiments of the present disclosure may be applied to any type of data generated by one or more sensors of image source104(e.g., LIDAR data, RADAR data, SONAR data, ultrasonic data, audio data, etc.).

In some embodiments, image source104may be a component of, or may be otherwise connected to, computing device102. For example, as described above, computing device102may be, or may be a component of, an endpoint device. In such embodiments, image source104may be a camera component of computing device102that is configured to generate an image and/or video data associated with the environment. In other or similar embodiments, image source104may be a device, or a component of or otherwise connected to a device, that is separate and distinct from computing device102. For example, as described above, computing device102may be, or may be a component of, a cloud computing platform or an edge device. In such embodiments, image source104may be a device (e.g., a surveillance camera, a device of an autonomous vehicle, etc.) that is connected to computing device102, data store112, and/or server machines130-150via network110or another network.

In some implementations, data store112is a persistent storage that is capable of storing content items (e.g., images) and data associated with the stored content items (e.g., object data, image metadata, etc.) as well as data structures to tag, organize, and index the content items and/or object data. Data store112may be hosted by one or more storage devices, such as main memory, magnetic or optical storage based disks, tapes or hard drives, NAS, SAN, and so forth. In some implementations, data store112may be a network-attached file server, while in other embodiments data store112may be some other type of persistent storage such as an object-oriented database, a relational database, and so forth, that may be hosted by computing device102or one or more different machines coupled to the computing device102via network110or another network.

Data store112may be or may include a domain-specific or organization-specific repository or data base. In some embodiments, computing device102, image source104, server machine130, server machine140, and/or server machine150may only be able to access data store via network110, which may be a private network. In other or similar embodiments, data stored at data store112may be encrypted and may be accessible to computing device102, image source104, server machine130, server machine140, and/or server machine150via an encryption mechanism (e.g., a private encryption key, etc.). In additional or alternative embodiments, data store112may be a publicly accessible data store that is accessible to any device via a public network.

Server machine130may include an image processing engine131that is configured to process data generated by image source104. For example, image source104and/or computing device102may encode image data (e.g., using a codec) generated by image source104prior to transmitting the image data to another device of system100via network110(or another network). Image processing engine131may decode the encoded image data (e.g., using the codec). In some embodiments, image processing engine131may re-encode decoded image data (e.g., using a different codec), prior to providing the image to another component or device of system100. In some embodiments, image process engine131may be configured to select, combine, and transmit signals (e.g., via a multiplexer component, etc.) associated with image data generated by image source104to another component or device of system100. In additional or alternative embodiments, image processing engine131may be configured to modify a quality of the image data generated by image source104before the image data is used for object detection and/or object tracking (e.g., by object detection engine141and/or object tracking engine151). For example, image processing engine131may be configured to apply one or more transformations to an image generated by image source104to remove or reduce an amount of noise present in the image, to crop the image, and so on. It should be noted that although some embodiments of the present disclosure provide that image processing engine131may modify a quality of image data, other components of system100(e.g., object detection engine141, object tracking engine151, etc.) may also be configured to modify the quality of the image data.

Server machine140may include an object detection engine141which is configured to detect one or more objects included in images depicting an environment, such as images generated by image source104. In some embodiments, object detection engine141may provide an image depicting an environment as input to a trained object detection model. The object detection model may be trained using historical data (e.g., historical images, historical object data, etc.) to detect an object (referred to here as a detected object) included in a given input image depicting an environment and estimate a region of the given input image that includes the detected object (referred to herein as a region of interest). In some embodiments, one or more outputs of the object detection model can indicate object data associated with the detected object. The object data may indicate a region of interest of a given input image that includes the detected object. For example, the object data can include a bounding box or another bounding shape (e.g., a spheroid, an ellipsoid, a cylindrical shape, etc.) that corresponds to the region of interest of the given input image. In some embodiments, the object data can include other data associated with the detected object, such as an object class corresponding to the detected object, mask data associated with the detected object (e.g., a two-dimensional (2D) bit array that indicates pixels (or groups of pixels) that corresponds to the detected object), and so forth.

Server machine150may include an object tracking engine151which is configured to track a state of one or more objects detected in one or more images (e.g., generated by image source104). For purposes of explanation, an object that is detected by object detection engine141is referred to herein as a detected object. An object that is tracked by object tracking engine151is referred to herein as a target object or a target. A state of a target, as provided herein, may correspond to a location of an object within an environment depicted by the one or more images, a position of the object within the environment, a scale or size of the object within the environment, a velocity of the object within the environment, and so forth.

In some embodiments, object tracking engine151may track a target based on an image including the target and object data (e.g., one or more bounding boxes) associated with the target. Object tracking engine151may instantiate an object tracker component (referred to as an object tracker herein) for each detected object in an image depicting the environment. An object tracker may be a logical component that is configured to maintain state data associated with a target within a set of images (e.g., a sequence of video frames) depicting the environment. For example, when an object is initially detected in an image (e.g., a video frame), object tracking engine151may instantiate an object tracker to monitor and determine a state associated with the detected object (referred to herein as a current state of the target). Object detection engine141may detect the target in other images depicting the environment (e.g., subsequent video frames) and the object tracker associated with the target may determine, for each of the other images, the current state of the target. The object tracker may update state data associated with the object to correspond to the determined current state and store the updated state data (e.g., at data store112). In some embodiments, the object tracker may further estimate a future state of the target in the environment and may store an indication of the future state (e.g., at data store112) with the updated state data. Further details regarding object tracking engine151are provided herein.

Object tracking engine151may include application modules that support one or more object tracking applications executing at devices of system100. As indicated above, an application module refers to a logical unit (e.g., a container, etc.) configured to execute a particular task associated with an object tracker application. A library refers to a collection of resources (e.g., configuration files, executable programs, etc.) allocated to support one or more application modules of an object tracker application. In some embodiments, server machine150(or another server machine of system100) may include an object tracker configuration engine152. Tracker configuration engine152may configure an object tracker application based on one or more configuration settings and/or configuration files (e.g., provided by a user of system100). In an illustrative example, a user of system100may request (e.g., via a user interface of a client device106) initialization of an object tracker application to track targets in a given environment. The requested object tracker application may be associated with one or more object tracker types (e.g., a location-based tracker, a visual feature-based tracker, a DCF-type tracker, etc.). The user may provide, via the user interface, an indication of configuration settings associated with one or more application modules to be executed by the object tracker application, in some embodiments. In other or similar embodiments, the user may provide, via the user interface, one or more configuration files associated with the application modules to be executed by the object tracker application. Each of the provided configuration files may include an indication of one or more configuration settings associated with a respective application module. Further details regarding the configuration settings and configuration files are provided below.

Tracker configuration engine152may obtain the configuration settings and/or configuration files provided by the user and may identify one or more libraries (e.g., from data store112) allocated to support each of the application modules for the application requested by the user. In response to identifying the one or more libraries, tracker configuration engine152may initialize a set of application modules that correspond to the one or more object tracker types for the requested application. In some embodiments, object tracking engine151may already be executing an object tracking application according to a set of application modules (i.e., that were previously initialized by tracker configuration engine152). In such embodiments, tracker configuration engine152may update the set of application modules associated with the object tracking application to include or remove one or more application modules, in accordance with the user request. In response to initializing (or updating) the set of application modules, tracker configuration engine152may configure the object tracking application to execute the set of application modules and provide the integrated, configured object tracking application, in accordance with the request. Further details regarding the tracker configuration engine152configuring the object tracking engine151to execute a particular set of application modules are provided in further detail herein.

As indicated above, a user of system100may provide an indication of one or more configuration settings and/or configuration files associated with an object tracking application via a user interface of client device106. Client device106may be, or may be a component of, devices, such as, but not limited to: televisions, smart phones, cellular telephones, personal digital assistants (PDAs), portable media players, netbooks, laptop computers, electronic book readers, tablet computers, desktop computers, set-top boxes, gaming consoles, a computing device for an autonomous vehicles, a surveillance device, and the like.

In some implementations, computing device102, image source104, client device106, data store112, and/or server machines130-150, may be one or more computing devices computing devices (such as a rackmount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores (e.g., hard disks, memories, databases), networks, software components, and/or hardware components that may be used to enable object detection based on an image. It should be noted that in some other implementations, the functions of computing device102, image source104, server machines130,140, and/or150may be provided by a fewer number of machines. For example, in some implementations server machines130,140, and/or150may be integrated into a single machine, while in other implementations server machines130,140, and150may be integrated into multiple machines. In addition, in some implementations one or more of server machines130,140, and150may be integrated into computing device102. For example, as illustrated inFIG.1, image processing engine131, object detection engine141, object tracking engine151and/or tracker configuration engine152may reside at on computing device102, in some embodiments. In general, functions described in implementations as being performed by computing device102and/or server machines130,140,150may also be performed on one or more edge devices (not shown) and/or client devices (not shown), if appropriate. In addition, the functionality attributed to a particular component may be performed by different or multiple components operating together. Computing device102and/or server machines130,140,150may also be accessed as a service provided to other systems or devices through appropriate application programming interfaces.

FIG.2is a block diagram of an example object monitoring pipeline200, according to at least one embodiment. As illustrated inFIG.2, pipeline200may include image source104, image processing engine131, object detection engine141, and/or object tracking engine151. In some additional or alternative embodiments, pipeline200may also include other engines, such as an object classification engine220, a post processing engine222, and so forth. As described with respect toFIG.1, image source104may be or may include one or more sensors (e.g., image sensors, etc.) that are configured to generate data associated with an environment. For example, image source104may be, or may include, a camera component that is configured to generate a video stream (i.e., a sequence of video frames or image frames) depicting the environment over a period of time. Image source104may generate an image202, in accordance with previously described embodiments, and may provide the image202to object detection engine141. In some embodiments, image source104may provide image202to image processing engine131. Image processing engine131may process image202, in accordance with previously described embodiments, and provide image202to object detection engine141.

In response to obtaining image202, object detection engine141may provide image202as input to a trained object detection model and obtain one or more outputs of the model that indicate object data204associated with one or more objects detected in image202, as previously described. The trained object detection model may be, for example, an artificial neural network such as a convolutional neural network trained to identify one or more types of objects, such as cars, people, animals, and so on. In some embodiments, object data204may include a bounding box (or a bounding shape) that indicates a region of image202that includes a detected object. Image202and/or object data204may be stored at a data store, such as data store112described with respect toFIG.1, data store350described with respect toFIG.3here, and/or data store650described with respect toFIGS.6A-6Dherein.

Object tracking engine151may be configured to track a state (e.g., a position, a location, a scale, a size, a velocity, etc.) of one or more targets included in an environment surveilled by image source104, as described above. In some embodiments, object tracking engine151may obtain image202and/or object data204from object tracking engine141, from image source104, and/or via a data store (e.g., data store112, data store350, data store650, etc.). Object tracking engine151may determine, based on the obtained image202and/or object data204, whether the environment depicted in image202includes an existing target that is tracked by an object tracker of object tracking engine151. If object tracking engine151determines that an existing target is detected in image202, the object tracker associated with the target may determine a state associated with the target in view of image202(e.g., current target state208). If object tracking engine151determines that an existing target is not detected in image202, object tracking engine151may determine to terminate the object tracker associated with the target (e.g., in accordance with a target termination policy for the intelligent video analytics system). Object tracking engine151may also determine whether a new object that is not currently tracked by an object tracker is included in image202. If so, object tracking engine151may instantiate a new object tracker to track the object, in accordance with embodiments described herein. The implementation and execution of object tracking engine151for a respective object tracking application may depend on a configuration of one or more application modules, as described herein.

As indicated above, tracker configuration engine152may initialize one or more application modules to execute tasks associated with tracking targets in an environment. In some embodiments, the one or more application modules may include a data association module230, a target manager module232, an object localization module234, a data extraction module236, a state estimation module238, a tracklet manager module240and/or one or more additional modules242A-N. Each of the one or more application modules may be supported by one or more libraries (e.g., stored at data store112, data store350, data store650, etc.), as described herein.

Data association module230may be configured to determine an association between an object detected in an environment (e.g., by object detection engine141or by object localization module234, in accordance with embodiments described below) and an existing target that is tracked by an object tracker of object tracking engine151. In some embodiments, data association module230may determine the association based on a proximity of a location that includes the detected object (e.g., indicated by one or more bounding boxes, etc.) and a location associated with an existing target (e.g., an estimated target location, a prior target location, etc.). In other or similar embodiments, data association module230may determine the association based on a comparison of other data (e.g., a similarity metric value, visual feature data, an identifier indicating one or more attributes, etc.) associated with the detected object and an existing target. Further details regarding the other data that data association module230may use to determine the association between a detected object and an existing target are provided below with respect to object localization module234and/or data extraction module236.

Data association module230may provide an indication of an association, or lack of an association, between a detected object and an existing target to target manager module232. In some embodiments, an association between a detected object and an existing target may indicate to target manager module232that the detected object corresponds to the existing target (e.g., the detected object is the same object as the existing target). Target manager module232may identify an object tracker that is configured to track a state of the target and provide data (e.g., object data204) associated with the target to the identified object tracker. The object tracker may update a state associated with the target based on the provided data, in accordance with embodiments provided herein. In other or similar embodiments, a lack of an association between a detected object and an existing target may indicate to target manager module232that a detected object is a new, previously unobserved object included in the environment (i.e., and an object tracker has not been instantiated to track the detected object) or that an existing target is not being detected and may no longer be present in the environment. For example, in response to obtaining one or more bounding boxes associated with an object detected in image202, data association module230may provide target manager module232an indication that the obtained bounding boxes do not correspond to an existing target. Accordingly, target manager module232may determine that the detected object is a new object and may instantiate an object tracker to track a state of the object, in accordance with embodiments described herein. In another example, data association module230may provide target manager module232that none of the obtained bounding boxes correspond to an existing target. Accordingly, target manager module232may determine that the existing target may no longer be present in the environment and may terminate an object tracker associated with the target (e.g., in accordance with a target termination protocol).

Object localization module234may be configured to estimate a location of existing targets (also referred to herein as localizing targets) tracked by object tracking engine151in a sequence of images202generated by image source104. Data extraction module236may be configured to extract data associated with one or more objects detected in an image202from the image202. The type of data that data extraction module236extracts from an image202may depend on which application modules are activated for a respective object tracking application and/or one or more configuration settings associated with the activated application modules. In some embodiments, the tasks performed by data extraction module236may be performed by object localization module234(i.e., the tasks for both modules are performed by a single module). This may be the case for some DCF-type object trackers, among other tracker types. In other or similar embodiments, the tasks performed by data extraction module236may be distinct from the tasks performed by object localization module234(i.e., data extraction related tasks are performed by data extraction module236and/or object localization related tasks are performed by object localization module234). This may be the case for some visual feature-based trackers, among other tracker types. In such embodiments, data extraction module236may be configured to perform additional tasks beyond data extraction, in accordance with embodiments provided below. It should be noted that although some embodiments of the present disclosure provide that some tasks may be performed by data extraction module236and other tasks may be performed by object localization module234, such embodiments may be performed if the tasks of data extraction module236and object localization module234are performed by a single application module, and vice versa.

In some embodiments, object localization module234may obtain object data204associated with an object detected in an image202and determine whether any object trackers have been instantiated to track targets in the environment depicted in image202. In one illustrative example, object localization module234may determine that no object trackers have been instantiated to track targets at the time object tracking engine151obtains image202. In such an example, object localization module234may provide an indication of one or more regions of image202that include detected objects (e.g., in view of object data204) to data extraction module236. Object localization module234may also provide to data extraction module236an indication of a type of visual features that are to be extracted from the one or more indicated regions of image202. The visual features may include an indication of one or more colors present in a set of pixels of a region of image202indicated by a bounding box (referred to herein as a bounding box region), a Histogram-of-Oriented-Gradient (HOG) of the bounding box region, or other visual features. Data extraction module236may extract the indicated visual features from the one or more regions of image202and may provide the extracted features to object localization module234(and/or store the extracted features in data store112, data store350, data store650, etc.).

In some embodiments, object detection engine141may not attempt to detect objects and generate corresponding object data204for each image202generated by image source104(e.g., in accordance with a protocol for the intelligent video analytics system). For example, object detection engine141may be configured to attempt to detect objects in every other image202generated by image source104. In such embodiments, object localization module234may be configured to detect and localize one or more objects depicted in image202using a correlation filter. A correlation filter refers to a class of classifiers that are configured to produce peaks in correlation outputs or responses. In some embodiments, a peak in a correlation output or response can correspond to an object depicted in image202. In some embodiments, a correlation filter can include at least one of Kernelized Correlation Filter (KCF), a discriminative correlation filter (DCF), a Correlation Filter neural network (CFNN), a Multi-Channel Correlation Filter (MCCF), a Kernel Correlation Filter, an adaptive correlation filter, and/or other types. A correlation filter may be implemented using one or more machine learning models, such as a machine learning model that uses linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, K-nearest neighbor (KNN), K-means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, long/short term memory/LSTM, Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models.

A correlation filter may be trained to produce or identify a peak correlation response at a region of an image that corresponds to a reference coordinate (e.g., a center) of an object depicted in the image. Object localization module234may obtain an image202(i.e., from image source104or via data store250) and apply the correlation filter to image202to obtain one or more outputs. The one or more outputs of the correlation filter can indicate one or more peak locations of a correlation response for image202(referred to herein simply as a correlation response). The locations of one or more correlation responses may correspond to regions of image202that depict an object in the environment and, in some embodiments, the peak location of the correlation response may correspond to the reference coordinate (e.g., the center) of the depicted object. Object localization module234may identify the regions of image202that are associated with a respective correlation response as regions of image202that depict a respective object (referred to herein as a correlation response region). Object localization module234may provide an indication of the regions to data extraction module236, in addition to or in lieu of an indication of regions of image202associated with one or more bounding boxes (referred to herein as bounding box regions), in accordance with previously described embodiments. Data extraction module236may extract visual features from the indicated correlation response regions and provide the extracted visual features to object localization module234, in accordance with previously described embodiments.

In some embodiments, object localization module234may apply the correlation filter to an image202even if object detection engine141generates object data204associated with image202. In such embodiments, object localization module234may use object data204and the output of the correlation filter to improve (i.e., re-train) the correlation filter for subsequent images (e.g., video frames) generated by image source104. For example, object localization module234may identify the correlation response regions of image202based on one or more outputs of the correlation filter. Object localization module234may compare the correlation response at the respective correlation response regions of image202to each bounding box indicated by object data204and determine an accuracy of the respective correlation responses based on the comparison. In some embodiments, object localization module234may provide an indication of the correlation responses, the bounding boxes indicated by object data, and/or the determined accuracy of each respective correlation responses to re-train the correlation filter.

In another illustrative example, object localization module234may determine that at least one object tracker has been instantiated to track targets at the time object tracking engine151obtains image202. In such embodiments, object localization module234may determine an estimated location for each target at the time object tracking engine151obtains image202. In some embodiments, object localization module234may estimate the location for a respective target based on a state (e.g., a prior state206, a current state208, and/or predicted state210) of the target. A prior state206of a target refers to a state (e.g., a position, a location, a scale, a size, a proximity, etc.) of the target during a time when one or more prior images depicting the environment had been generated. A current state208of the target refers to a state of the target during a time when image202is generated. A predicted state208of the target refers to a state of the target that is estimated for the target at the time image202is generated based on one or more prior states of206of the target. In some embodiments, the prior state206, the current state208, and/or the predicted state210of a target may be determined by state estimation module238, or by other modules in view of object data204(e.g., data association module230, additional modules242A-N, etc.). Further details regarding determining the state of a target are provided herein.

As indicated above, object localization module234may determine an estimated location for a target based on one or more states of the target. For example, in some embodiments, object localization module234may obtain (e.g., from data store112, data store350, data store650, etc.) a predicted state for a target. The predicted state may correspond to a location or position of the target at a time that image202is generated. Object localization module234may estimate the location of the target in image202based on the predicted location or position of the target. In another example, object localization module234may obtain a prior state for the target, which may correspond to a prior location, position, and/or velocity of the target. Object localization module234may estimate the location of the target in image202based on the prior location and/or position and the prior velocity of the target, in some embodiments. In response to determining the estimated location of the target in image202, object localization module234may provide an indication of a region of image202that corresponds to the estimated location (and an indication of a type of visual features to be extracted) to data extraction module236, as described above. Object localization module234may also provide an indication of a bounding box region and/or a correlation response region associated with a detected object to data extraction module236receive, from data extraction module, visual features from the bounding box region and/or the correlation response region, in accordance with previously described embodiments.

Object localization module234may generate a set of similarity metric values each indicating a similarity between a detected object (e.g., from image202) and an existing target. As described above, object localization module234may determine that no object trackers have been instantiated for targets at the time image202is obtained, and therefore object tracking engine151may not be tracking any targets. Accordingly, object localization module234may assign the detected object a particular similarity metric (e.g., a low similarity metric value), which indicates that the detected object does not correspond to an existing target. In other or similar embodiments, object localization module234may determine that at least one object tracker has been instantiated for a target in the environment. In such embodiments, object localization module234may compare the visual features of the detected object (i.e., extracted from the bounding box region and/or the correlation response region of image202) with the visual features associated with each target tracked by object tracking engine151and determine a similarity metric value associated with the extracted visual features based on the comparison. In response to determining that the visual features of the detected object correspond to (e.g., match or essentially match) the visual features associated with a respective target, object localization module234may assign a high similarity metric value to the detected object and the target. In response to determining that the visual features of the detected object do not correspond to the visual features associated with the target, object localization module234may assign a low similarity metric value to the detected object and the target. Object localization module234may generate the set of similarity metric values based on the determined similarity between each detected object and each target tracked by object tracking engine151.

Object localization module234may provide an indication of one or more bounding box regions (and/or correlation response regions), an indication of the estimated target locations, and the set of similarity metric values to data association module230. Data association module230may determine an association between a detected object and an existing target in view of the data provided by object localization module234, in some embodiments. For example, data association module230may determine the association, or lack of an association, between a detected object and a target by determining a proximity of a bounding box region (and/or correlation response region) to an estimated target location, in accordance with previously described embodiments. In some embodiments, data association module230may also determine the association based on a similarity metric value between the visual features of the object detected in the bounding box region and/or correlation response region and the visual features of an existing target associated with an estimated location. For example, data association module230may determine that a bounding box region associated with a detected object is located within a threshold proximity of a region of image202associated with an estimated target location. However, data association module230may determine that there is no association between the detected object and the existing target if a similarity metric value determined for the detected object and the existing target do not satisfy a similarity criterion (e.g., the similarity metric value falls below a threshold value). Alternatively, data association module may determine that there is an association between the detected object if the determined similarity metric value satisfies the similarity criterion (e.g., the similarity metric value exceeds the threshold value). Data association module230may determine the association, or lack of association, in view of the above, and may provide an indication of the association or lack of association to target manager module232, in accordance with previously described embodiments.

It should be noted that although some embodiments of the present disclosure are directed to localizing visual features of detected or depicted objects to existing targets, other techniques may be used to localize the existing targets. For example, data extraction module236may extract visual features from bounding box regions (and/or correlation response regions) of image202, in accordance with previously described embodiments. Data extraction module236, object localization module234, and/or another module242of object tracking engine151may provide the extracted visual features as input to a machine learning model (e.g., a recurrent neural network, etc.) and extract, from the one or more obtained outputs, an identifier associated with one or more attributes of the extracted visual features. Data extraction module236, object localization module234, and/or another module242may compare the extracted identifier to identifiers associated with existing targets and may provide an indication of the comparison to data association module214, in some embodiments. Data association module214may determine an association, or lack of association, between a detected object and an existing target based on the identifier comparison, in accordance with previously described embodiments. For example, if a bounding box region is located within a threshold proximity to an estimated location region and an identifier associated with the detected object corresponds to an identifier for an existing target, data association module214may determine that the detected object is associated with the existing target and may provide an indication of the association to target manager module232.

As indicated above, data extraction module236and/or object localization module234may be configured to extract different types of object data from image202and/or may be configured to implement different types of object localization techniques to localize a target tracked by object tracking engine151. Tracker configuration engine151may configure data extraction module236and/or object localization module234to extract a particular type of object data and/or implement a particular type of object localization technique in view of one or more settings and/or configuration files associated with an object tracking application. Further details regarding object tracking application configuration are provided with respect toFIG.3.

As indicated above, an object tracker instantiated for a target may be configured to track a state of the target in an environment. A target state may refer to a location, a position, a scale or size, a velocity, etc., associated with a target during a time period that one or more images202are generated. In some embodiments, an object tracker may determine one or more target states (e.g., prior target state206, current target state208, future target state210, etc.) based on state estimation and/or predictions made by state estimation module238. State estimation module238may be configured to estimate a state of a target in a given image based on state data associated with the target at the time an image202depicting the target is generated. For example, a current target state206may be defined by one or more coordinates (e.g., Cartesian coordinates, etc.) for a bounding box associated with the target in image202, a size of the bounding box associated with the target, and/or a change in the one or more coordinates for the bounding box relative to prior coordinates of a bounding box associated with the target in one or more prior images depicting a surveilled environment. In another example, the current target state206may be further defined by a change in the size of the bounding box associated with the target relative to a bounding box associated with the target in the one or more prior images. In some embodiments, the current target state206may also include one or more target features (e.g., extracted from the bounding box region of image202, extracted from a correlation response region of image202, etc.).

In some embodiments, state estimation module238may determine a current target state206based on data obtained for the target from image202. For example, an object tracker, data association module230and/or object localization module234may provide an indication of one or more bounding boxes associated with the target to state estimation module238, in some embodiments. State estimation module238may determine the coordinates of the one or more bounding boxes and/or the size of the one or more bounding boxes based on the provided data. In some embodiments, state estimation module238may determine whether the target is a new target in image202or the target is an existing target that was tracked before image202was generated. In response to determining that the target was an existing target, state estimation module238may obtain prior target state data206for the target (e.g., from data store112, data store350, data store650, etc.). Prior target state data206refers to target state data that is estimated (e.g., by state estimation module238) for a target based on images generated prior to image202. State estimation module238may determine the change in the one or more coordinates for the bounding box associated with the target by determining a distance between the one or more coordinates of the bounding box associated with image202and coordinates of a bounding box associated with the target depicted in one or more prior images. State estimation module238may determine a speed and direction (i.e., a velocity) at which the target is moving based on the determined distance. In some embodiments, state estimation module238may further determine a change in the size or scale of the target based the determined distance.

As indicated above, the change in the one or more coordinates for the bounding box associated with the target depends on the location of a bounding box for an image generated prior to image202. Accordingly, if a target is a new target in image202, state estimation module238may not determine the change in the location and/or size or scale of the target (i.e., as no prior images generated by image source104depict the target). If the target is depicted in subsequent images of the surveilled environment, state estimation module238may determine the velocity and/or size or scale change of the target when the subsequent images are generated, in accordance with previously described embodiments.

It should be noted that in some embodiments described herein, object localization module234may identify one or more correlation response regions of image202(e.g., using a correlation response filter, etc.). In such embodiments, state estimation module238may determine the current state of the target based on the identified correlation response regions in addition to or in lieu of the bounding box regions of image202.

State estimation module238may store the coordinates of the one or more bounding boxes, the coordinates of one or more correlation response regions, the size of the one or more bounding boxes and/or the correlation response region, the velocity of the target, and/or the change in size or scale of the target as current target state208(e.g., in data12, data store350, data store650, etc.). In some embodiments, state estimation module238may be further configured to predict a future state of the target in the environment based on the current target state208for the target. In some embodiments, state estimation module238may obtain the current target state206and provide the current target state206as an input to one or more state prediction functions. A state prediction function may be configured to execute a recursive filter, such as a Kalman Filter (KF), to estimate a future state of a target in the environment. State estimation module238may obtain an output from the one or more state prediction functions and determine, based on the output, a future state of the target during a time that is subsequent to when image202is generated, in some embodiments. In other or similar embodiments, state estimation module238may determine multiple future states of the target during a time period that is subsequent to when image202is generated. For example, state estimation module238may determine, based on the output of the one or more state prediction functions, a future state of the target at each instance of time of a time period that is subsequent to when image202is generated. State estimation module238may store the one or more future states of the target (e.g., at data store112, data store350, data store650, etc.) as predicted target state210.

In additional or alternative embodiments, state estimation module238may use one or more machine learning models to predict the future state of the target. The one or more machine learning models may include a long term short term memory (LSTM) model, or another type of recurrent neural network (RNN) model. In some embodiments, the one or more machine learning models may be trained using historical object data and/or historical target state data to predict a future state of a target based on given target state and/or object data. State estimation module238may provide object data204, the prior target state206and/or the current target state208for a target as input to the one or more machine learning models and may obtain an output of the one or more models. State estimation module238may extract, from the one or more outputs, multiple sets of state data for the target. Each set of target state data may correspond to a future state of the target at an instance of time that is subsequent to when image202is generated. In some embodiments, state estimation module238may also extract an indication of a level of confidence that a respective set of state data corresponds to the target. State estimation module238may identify one or more sets of state data associated with a level of confidence that satisfies a level of confidence criterion. For example, state estimation module238may identify a set of state data that is associated with the higher level of confidence than other sets of state data extracted from the one or more outputs. In another example, state estimation module238may identify each set of state data associated with a level of confidence that meets or exceeds a threshold level of confidence. Responsive to identifying the one or more sets of state data, state estimation module238may store the one or more sets of state data as predicted target state210, as described above.

In some embodiments, state estimation module238may provide one or more determined target states (e.g., prior target state206, current target state208, predicted target state210, etc.) to other modules of object tracking engine151. For example, state estimation module238may provide an indication of the one or more target states to object localization module234. Object localization module234may use the provided target state(s) to improve (i.e., re-train) the correlation filter for subsequent images generated by image source104, as described above. In other or similar embodiments, data association module230may use a target state information to determine an association between a detected object and an existing target, as described above. State estimation module238may provide the one or more determined target states to data association module230(e.g., in response to a request from data association module230, etc.).

As illustrated inFIG.2, object tracking engine151may include other application modules. For example, object tracking engine151may include a tracklet manager module240that is configured to generate and maintain one or more tracklets associated with targets tracked by object tracking engine151. A tracklet refers to a set of coordinates that indicates a path that is taken, or is predicted to be taken, by a target in a surveilled environment. Tracklet manager module240may use the one or more tracklets to determine whether a lost target (i.e., a target that is not detected in one or more images202) corresponds to a newly detected object in the environment, and if so, cause the object tracker for the lost target to track the target in view of the newly detected object. Object tracking engine151may include any number of additional application modules, such as application modules242A-N. Application modules242A-N may be configured to perform one or more of the tasks provided in the present description, or any other tasks not provided in the present description. In an illustrative example, a user of the intelligent video analytics system may program or otherwise develop a new type of application module (e.g., a module or application configured to perform a new type of task, to extract a different type of data from image202, and/or to localize targets according to a different technique, etc.). The user may provide (e.g., via client device106) one or more configuration files associated with the new type of application module and tracker configuration engine152may initialize the application module to execute in an object tracking application that is supported by object tracking engine151, in accordance with embodiments described herein.

Embodiments of the present disclosure discuss several different types of application modules that may be executed by object tracking engine151to support an object tracking application (e.g., data association module230, target manager module232, object localization module234, data extraction module236, state estimation module238, tracklet manager module240, additional modules242A-N, etc.). It should be noted that in some embodiments, one or more application modules may be executed by object tracking engine151than are illustrated inFIG.2. In other or similar embodiments, fewer application modules may be executed by object tracking engine151than are illustrated inFIG.2. Object tracking engine151may execute at least data association module230and target manager module232for each object tracking application that is configured by tracker configuration engine152. Object tracing engine151may execute additional application modules (e.g., object localization module, data extraction module236, state estimation module238, etc.) in view of one or more configuration settings and/or configuration files (e.g., that are provided by a user of the intelligent video analytics system via client device106).FIGS.6A-6Dillustrate example object tracking engine151configurations, however additional configurations may be possible.

As indicated above, object tracking engine151may determine a state (e.g., prior state206, current state208, predicted state210) associated with a target. Object tracking engine151may provide an indication of the determined state to other engines of pipeline200, as illustrated inFIG.2. For example, object tracking engine151may provide an indication of the determined state to an object classification engine220, which is configured to determine an object class associated with a detected object or target, in some embodiments. In another example, object tracking engine151may provide an indication of the determined state to a post processing engine222, which is configured to perform one or more post-processing functions to image202before data associated with image202and/or the detected objects or targets in image202are provided to a user of the intelligent video analytics system (e.g., via client device106).

FIG.3is a block diagram of an example tracker configuration engine152, according to at least one embodiment. As described above, tracker configuration engine152may configure one or more application modules for an object tracking application running on object tracking engine151. In some embodiments, tracker configuration engine152may configure the one or more application modules based on one or more configuration settings302and/or one or more configuration files304that are provided by a user via client device106.

In some embodiments, a user of a client device106may provide an indication of configuration settings302via a graphical user interface (GUI), such as GUI700ofFIG.7. Configuration settings302may include an indication of a type of tracker to be implemented for an object tracking application, an indication of one or more modules that are to be activated for an object tracking application, and/or values for one or more configuration parameters associated with executing the respective modules. For example, if a user wishes to activate object localization module234for the object tracking application, the configuration parameters associated with the object localization module234may include an indication of the object localization technique that is to be implemented by the object localization module234(e.g., an indication of whether object localization module234is to implement a correlation filter or a recurrent neural network to perform object localization, etc.), an indication of a type and/or characteristics of visual features that are to be used by object localization module234to implement the localization technique, an indication of settings associated with one or more machine learning models that are used to implement the localization technique, an indication of one or more characteristics associated with image202and/or a prior image that includes an existing target, and so forth. In another example, if a user wishes to activate data extraction module236, the configuration parameters associated with data extraction module236may include an indication of a type and/or characteristics of visual features that are to be extracted from an image202, characteristics associated with images that are to be used for the future extraction, and so forth. In some embodiments, tracker configuration engine152may obtain configuration settings302from client device106without obtaining configuration files302.

In other or similar embodiments, a user of a client device106may provide one or more configuration files302associated with one or more application modules that are to be activated for an object tracking application. A configuration file304refers to a file that includes one or more configuration settings302and/or additional configuration information associated with an application module of object tracking engine151that is to be used by tracker configuration engine152to initialize the module. Each configuration file304may correspond to a respective application module and, in some embodiments, each application module may be supported by multiple configuration files304. In some embodiments, a single configuration file304may include data for multiple application modules (e.g., for all application modules). In some embodiments, a configuration file304may include an indication of a type of application tracker that the file304is associated with, a type of module that the file304is associated with, and/or one or more configuration parameter values associated with executing the module. The configuration parameter values may include parameter values that are the same or similar to the values described with respect to settings302, in some embodiments. In additional or alternative embodiments, the configuration parameter values may include one or more parameter values associated with enabling communication between the module and other modules of the object tracking application. For example, a user may provide (via client device106) a first configuration file associated with executing object localization module234and second configuration file associated with executing data association module230. The first and second configuration files may include configuration parameter values for executing particular tasks associated with the respective modules. In addition, the first configuration file may include additional configuration parameter values associated with enabling object localization module234to communicate with data association module230(e.g., parameter values that indicate the type of data that is to be transferred from object localization module234to data association module230). The second configuration file may similarly include configuration parameter values associated with enabling data association module230to communicate with object localization module234(e.g., parameter values that indicate the type of data that data association module230is to receive from object localization module234).

As indicated above, a user may be enabled to execute a custom object tracker application by providing one or more configuration files associated with a new application module. In some embodiments, the one or more configuration files that configure the new application module may update object tracking engine151to correspond to a new type of custom object tracker.

Tracker configuration engine152may store the configuration settings302and/or configuration file(s)304obtained from client device106at data store350. In some embodiments, data store350may correspond to data store112described with respect toFIG.1and/or data store650described with respect toFIG.6. Library identification component310may identify one or more libraries352for an application module associated with the obtained settings302and/or files304. As indicated above, an application module library352refers to a set of resources, such as executable program files, configuration file(s)354, etc., that are used to execute an application module for an object tracking application. If client device106only provides an indication of a type of object tracker that the user wishes to implement, library identification component310may identify one or more libraries352for application modules associated with the object tracker type (e.g., defined by a developer or operator of the intelligent video analytics system, etc.). Alternatively, if client device106provides an indication of the one or more particular application modules that the user wishes to implement, library identification component310may identify libraries associated with the particular application modules.

In response to library identification component310identifying a library352associated with the obtained settings302and/or file(s)304, module initialization component312may initialize the application module by updating a configuration file354of the identified library352or libraries to correspond to the obtained settings302and/or file(s)304. If tracker configuration engine152obtains a configuration file304associated with the module, module initialization component312may update configuration file(s)354to correspond to the obtained configuration file304(e.g., replace configuration files354with obtained configuration files304). If tracker configuration engine152obtains configuration settings302associated with the module instead of configuration files304, module initialization component312may update configuration file(s)354in view of the obtained settings302. For example, module initialization component312may update a corresponding setting of configuration file354to correspond to a respective setting of obtained settings302. As indicated above, in some embodiments, obtained configuration settings302may only include an indication of a type of object tracker that a user chooses to implement and/or one or more particular application modules that the user chooses to implement (e.g., without indication of one or more parameter values associated with executing the application modules). In some embodiments, module initialization component312may be configured to initialize the configuration files354of the identified libraries352according to one or more default settings (e.g., defined by a developer or operator of the intelligent video analytics system, etc.).

Tracker initialization component314may initialize an object tracker application based on the application modules initialized by module initialization component312. In some embodiments, tracker initialization component312may initialize the object tracker application by updating parameter values of one or more configuration file(s)354associated with the object tracking application to enable communication between the modules. For example, if object localization module234is activated for an object tracking application, tracker initialization component314may update one or more parameter values of configuration file(s)354associated with object localization module234to enable communication to data association module230, data extraction module236, etc., as described above. Similarly, tracker initialization component314may update one or more parameter values of configuration file(s)354associated with data association module230, data extraction module236, etc. to enable communication with object localization module234. In some embodiments, obtained configuration file(s)304may include one or more parameter values associated with enabling communication between different application modules of the object tracker application. In such embodiments, tracker initialization component314may not update the configuration file(s)354for the activated modules (i.e., as file(s)354already enable communication between the different application modules).

Tracker testing component316may implement one or more tests for the initialized object tracking application before the object tracking application is executed to track targets in a surveilled environment. For example, tracker testing component216may provide test data (not shown) including one or more test images and/or test object data to the initialized object tracking application. Tracker testing component216may evaluate the execution of the object tracking application for the provided test data to detect any errors associated with the object tracking application. If any errors are detected, tracker testing component216may update one or more parameter values included in one or more configuration file(s)354to address the errors, in some embodiments. In other or similar embodiments, tracker testing component216may transmit a notification to client device106indication that the object tracking application, as configured, cannot be executed. If tracker testing component216does not detect any errors associated with the object tracking application, tracker configuration engine152may provide the object tracking application to be used to track targets in a surveilled environment, in accordance with previously described embodiments.

FIGS.4and5are flow diagrams of example methods400and500, respectively, which are related to configuring an object detection engine, according to at least some embodiments. In at least one embodiment, methods400and500may be performed by computing device102, client device106, server machine130, server machine140, server machine150, one or more edge devices, one or more endpoint devices, or some other computing device, or a combination of multiple computing devices. Methods400and500may be performed by one or more processing units (e.g., CPUs and/or GPUs), which may include (or communicate with) one or more memory devices. In at least one embodiment, methods400and500may be performed by multiple processing threads (e.g., CPU threads and/or GPU threads), each thread executing one or more individual functions, routines, subroutines, or operations of the method. In at least one embodiment, processing threads implementing methods400and500may be synchronized (e.g., using semaphores, critical sections, and/or other thread synchronization mechanisms). Alternatively, processing threads implementing methods400and500may be executed asynchronously with respect to each other. Various operations of methods400and500may be performed in a different order compared with the order shown inFIGS.4and5. Some operations of the methods may be performed concurrently with other operations. In at least one embodiment, one or more operations shown inFIGS.4and5may not always be performed.

FIG.4illustrates a flow diagram of an example method400of configuring an object tracker for an intelligent video analytics system, according to at least one embodiment. In some embodiments, one or more operations of method400may be performed by one or more components of tracker configuration engine152, described herein. Processing units performing method400may execute a first set of application modules for an object tracking application configured to track a state of objects in an environment based on images depicting the environment. In some embodiments, the first set of application modules may be associated with a first object tracker type. For example, the first set of application modules may be associated with a location-based tracker type. Accordingly, the first set of application modules may include data association module230and target manager module232.

At block412, processing units performing method400may receive a request to configure the object tracking application to execute a second set of application modules. The second set of application modules may be associated with a second object tracker type. In some embodiments, the request may be received from client device106. In such embodiments, the request may include one or more configuration settings302and/or configuration files304. In accordance with the previous example, the second set of application modules may correspond to a DCF-type tracker. Accordingly, the second set of application modules may include data association module230, target manager module232, object localization module234, data extraction module236and/or state estimation module238.

At block414, processing units performing method400may configure the object tracking application to execute the second set of application modules in accordance with the request. Processing units performing method400may configure the object tracking application in accordance with embodiments described with respect toFIG.3. For example, module initialization component312of tracker configuration engine152may update configuration file(s)354for one or more application modules of the second set of application modules in view of the settings302and/or file304included in the request. Tracker initialization component314may update one or more additional parameter values of the file(s)354to enable communication between each of the second set of application modules, in accordance with previously described embodiments.

In some embodiments, configuring the object tracking application may involve enabling or disabling application modules of the first set of application modules that are different from the second set of application modules. For example, if the first set of application modules is associated with a location-based tracker and the second set of application modules is associated with a DCF-type tracker, tracker configuration engine152may enable (i.e., activate) object localization module234, data extraction module236and/or state estimation module238in configuring the object tracking application. If, however, the first set of application modules is associated with a DCF-type tracker and the second set of application modules is associated with a location-based tracker, tracker configuration engine152may disable (i.e. deactivate) object localization module234, data extraction module236and/or state estimation module238in configuring the object tracking application.

At block416, processing units performing method400may execute the second set of application modules for the object tracking application to track the state of the objects in the environment based on the images depicting the environment. In some embodiments, processing units performing method400may execute the second set of application modules by providing the object tracking application for execution via object tracking engine151.

As indicated with respect toFIG.4, in some embodiments, an object tracking application may operate in view of a first set of application modules of a first type and a user may request, during operation, reconfiguration of the object tracking application in view a second set of application modules of a second type. In some embodiments, after tracker configuration module152reconfigures the object tracking application in view of the second set of application modules, the object tracking application may activate, via object tracking engine151, one or more new object trackers for the targets according to the updated configuration files for the reconfigured object tracking application. In other or similar embodiments, the object tracking application may execute the previous object trackers for the targets according to the prior configuration files and may instantiate new object trackers to track new targets that are detected in images202. The previous object trackers may eventually track targets according to the updated configuration files (e.g., based on new or updated state data that is collected for the targets, etc.).

FIG.5illustrates a flow diagram of another example method500of configuring an object tracker for an intelligent video analytics system, according to at least one embodiment. In some embodiments, one or more operations of method500may be performed by one or more components of tracker configuration engine152, described herein. Processing units performing method500may receive a request to configure an object tracking application to execute a set of application modules associated with a particular object tracker type. Processing units performing method500may receive the request in accordance with previously described embodiments. At block512, processing units performing method500may initialize a set of application modules based on one or more libraries that correspond to the particular object tracker type. As described above, library identification component310may identify module libraries352for the set of application modules (e.g., via data store350). Module initialization component312may initialize the set of application modules by updating one or more configuration files354of the identified libraries352to correspond to settings302and/or files304of the received request.

At block514, processing units performing method500may configure the object tracking application to execute the initialized set of application modules. Tracker initialization component314may update one or more parameters to enable communication between the initialized set of application modules, as described above. At block516, processing units performing method500may provide the configured object tracking application (e.g., to the requestor, for execution via object tracking engine151, etc.) in accordance with the request.

FIGS.6A-Dillustrate example object tracking engines151for applications that are configured by tracker configuration engine152, according to at least one embodiment.FIG.6Aillustrates an example object tracking engine151for an application configured to execute a location-based tracker. As illustrated inFIG.6A, object tracking engine151may include data association module230and target manager module232. As described with respect toFIG.2, object tracking engine151may be configured to obtain object data204associated with image202from object detection engine141and determine whether an object associated with obtained object data204(i.e., current object data654at data store650) is associated with an existing target tracked by object tracker engine151. In some embodiments, data association module230may determine whether the object is associated with the existing target based on prior object data204associated with the existing target and current object data associated with the detected object. Data association module230may provide an indication of an association, or a lack of an association, between the object and the existing target manager module232. Target manager module232may instantiate an object tracker for the detected object, terminate an object tracker for the detected object, and/or cause an object tracker to update a state for the existing target based on the received indication, in accordance with previously described embodiments.

FIG.6Billustrates an example object tracking engine151for an application configured to execute another type of object tracker (e.g., a “simple, online, real-time object tracker” or “SORT” tracker). As illustrated inFIG.6B, object tracking engine151may include data association module230, target manager module232, and state estimation module238. Data association module230and target manager module232may be configured to track targets in accordance with embodiments described with respect toFIG.2andFIG.6A. Data association module230and target manager module232may also be configured to provide state data to state estimation module238, which may be configured to determine a prior target state206, a current target state208, and/or a predicted target state210associated with the target, in accordance with previously described embodiments. In some embodiments, data association module230may be further configured to determine an association between a detected object and an existing target based on a prior target state206and/or a predicted target state210associated with the existing target.

FIG.6Cillustrates an example object tracking engine151for an application configured to execute a visual-feature based tracker and/or a DCF-type tracker. As illustrated inFIG.6C, object tracking engine151may include data association module230, target manager module232, state estimation module238, object localization module234, and/or data extraction module236. In some embodiments, object localization module234may be configured to localize existing targets in view of image202and object data204, in accordance with previously described embodiments. Object localization module234may implement a DCF model to localize the existing targets and/or a recurrent neural network to localize the existing targets, depending on the configuration settings associated with the configuration files for the application. Data extraction module236may be configured to extract data from image202that is used by object localization module234to localize the exiting targets, in accordance with previously described embodiments. Object localization module234may provide an indication of bounding box regions (or correlation response regions), estimated target locations, and/or similarity metrics to data association module230, in accordance with previously described embodiments. Data association module, target manager module232and state estimation module238may perform similar tasks as provided herein.

FIG.6Dillustrates an example object tracking engine151for an application configured to execute any of the previously described trackers, with the added functionality of tracklet management. In some embodiments, the object tracking engine151ofFIG.6Dmay be for an application configured to execute a custom object tracker. As illustrated inFIG.6D, object tracking engine151may include the same or similar modules as included for the object tracking engines151illustrated inFIGS.6A-6C, as well as a tracklet manager module240. Tracklet manager module240may be configured to generate a current target tracklet642and/or a predicted target tracklet244for one or more targets tracked by object tracking engine151. Tracklet manager module240may generate tracklets642and/or644based on prior target state206, current target state208, and/or predicted target state210. In response to detecting that a target is lost (i.e., a target is not detected in a current image depicting an environment), tracklet manager module240may compare a predicted target tracklet644for a lost target to a current target tracklet642associated with a new object detected in image202. If target manager module240determines that the tracklet644corresponds to the tracklet642, tracklet manager module240may determine that the new object is the same as the lost target and may cause an object tracker associated with the lost target to track the target in view of state data obtained for the new object.

As indicated above,FIGS.6A-6Dillustrate example object tracking engines151for applications configured in accordance with embodiments of the present disclosure. It should be noted that other types of application modules and/or other types of configurations may be implemented in accordance with embodiments of the present disclosure. The examples provided byFIGS.6A-6Dare merely provided for illustrative purposes and are not intended to be limiting whatsoever.

At any time, processing logic may adjust configuration settings of one or more configuration files to transition between any of the example tracking engines ofFIGS.6A-6Dand/or other types of tracking engines. The various application modules may be part of a single unified tracking framework, in which application modules for the tracking engine may be activated, deactivated and/or reconfigured based on an update to one or more configuration files.

FIG.7illustrates an example graphical user interface (GUI)700that enables a user of an intelligent video analytics system to provide one or more settings for configuring an application executing on object tracking engine151, according to at least one embodiment. GUI700may be provided to a user of the intelligent video analytics system, via client device106, in accordance with previously described embodiments. GUI700may provide one or more first sections that enable a user to select a particular type702of object tracker to be implemented for the object tracking application. For example, a user may engage with a GUI element associated with object tracker type A if the user wishes to implement an object tracker that includes a data association module230and a target manager module232(i.e. a location-based tracker). In another example, a user may engage with a GUI element associated with object tracker type C if the user wishes to implement an object tracker that includes a data association module230, a target manager module232, an object localization module234, and a data extraction module236. In some embodiments, the user may interact with another GUI element to indicate the type of data that the user wishes to be extracted from images by data extraction module236. In other or similar embodiments, the user may interact with yet another GUI element to indicate the type of object localization technique that the user wishes the object localization module234to implement (not shown).

In additional or alternative embodiments, GUI700may provide one or more second sections that enable a user to select particular application modules704that the user wishes to be implemented by the configured object tracking application. For example, the user may select respective GUI elements associated with a data association module230and target manager module232if the user wishes to configure an object tracker that implements these modules. The user may also configure a custom object tracker by selecting respective GUI elements associated with modules that the user wishes to configure.

Inference and Training Logic

FIG.8Aillustrates inference and/or training logic815used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic815are provided below in conjunction withFIGS.8A and/or8B.

In at least one embodiment, inference and/or training logic815may include, without limitation, a code and/or data storage805to store backward and/or output weight and/or input/output data corresponding to neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and/or data storage805stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during backward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, training logic815may include, or be coupled to code and/or data storage805to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, any portion of code and/or data storage805may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of code and/or data storage805may be internal or external to on one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or data storage805may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or data storage805is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, code and/or data storage801and code and/or data storage805may be separate storage structures. In at least one embodiment, code and/or data storage801and code and/or data storage805may be same storage structure. In at least one embodiment, code and/or data storage801and code and/or data storage805may be partially same storage structure and partially separate storage structures. In at least one embodiment, any portion of code and/or data storage801and code and/or data storage805may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, inference and/or training logic815may include, without limitation, one or more arithmetic logic unit(s) (“ALU(s)”)810, including integer and/or floating point units, to perform logical and/or mathematical operations based, at least in part on, or indicated by, training and/or inference code (e.g., graph code), a result of which may produce activations (e.g., output values from layers or neurons within a neural network) stored in an activation storage820that are functions of input/output and/or weight parameter data stored in code and/or data storage801and/or code and/or data storage805. In at least one embodiment, activations stored in activation storage820are generated according to linear algebraic and or matrix-based mathematics performed by ALU(s)810in response to performing instructions or other code, wherein weight values stored in code and/or data storage805and/or code and/or data storage801are used as operands along with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, any or all of which may be stored in code and/or data storage805or code and/or data storage801or another storage on or off-chip.

In at least one embodiment, ALU(s)810are included within one or more processors or other hardware logic devices or circuits, whereas in another embodiment, ALU(s)810may be external to a processor or other hardware logic device or circuit that uses them (e.g., a co-processor). In at least one embodiment, ALUs810may be included within a processor's execution units or otherwise within a bank of ALUs accessible by a processor's execution units either within same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.). In at least one embodiment, code and/or data storage801, code and/or data storage805, and activation storage820may be on same processor or other hardware logic device or circuit, whereas in another embodiment, they may be in different processors or other hardware logic devices or circuits, or some combination of same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage820may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. Furthermore, inferencing and/or training code may be stored with other code accessible to a processor or other hardware logic or circuit and fetched and/or processed using a processor's fetch, decode, scheduling, execution, retirement and/or other logical circuits.

In at least one embodiment, activation storage820may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, activation storage820may be completely or partially within or external to one or more processors or other logical circuits. In at least one embodiment, choice of whether activation storage820is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. In at least one embodiment, inference and/or training logic815illustrated inFIG.8Amay be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic815illustrated inFIG.8Amay be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as data processing unit (“DPU”) hardware, or field programmable gate arrays (“FPGAs”).

FIG.8Billustrates inference and/or training logic815, according to at least one or more embodiments. In at least one embodiment, inference and/or training logic815may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, inference and/or training logic815illustrated inFIG.8Bmay be used in conjunction with an application-specific integrated circuit (ASIC), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic815illustrated inFIG.8Bmay be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware or other hardware, such as data processing unit (“DPU”) hardware, or field programmable gate arrays (FPGAs). In at least one embodiment, inference and/or training logic815includes, without limitation, code and/or data storage801and code and/or data storage805, which may be used to store code (e.g., graph code), weight values and/or other information, including bias values, gradient information, momentum values, and/or other parameter or hyperparameter information. In at least one embodiment illustrated inFIG.8B, each of code and/or data storage801and code and/or data storage805is associated with a dedicated computational resource, such as computational hardware802and computational hardware806, respectively. In at least one embodiment, each of computational hardware802and computational hardware806comprises one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and/or data storage801and code and/or data storage805, respectively, result of which is stored in activation storage820.

In at least one embodiment, each of code and/or data storage801and805and corresponding computational hardware802and806, respectively, correspond to different layers of a neural network, such that resulting activation from one “storage/computational pair801/802” of code and/or data storage801and computational hardware802is provided as an input to “storage/computational pair805/806” of code and/or data storage805and computational hardware806, in order to mirror conceptual organization of a neural network. In at least one embodiment, each of storage/computational pairs801/802and805/806may correspond to more than one neural network layer. In at least one embodiment, additional storage/computation pairs (not shown) subsequent to or in parallel with storage computation pairs801/802and805/806may be included in inference and/or training logic815.

Data Center

FIG.9illustrates an example data center900, in which at least one embodiment may be used. In at least one embodiment, data center900includes a data center infrastructure layer910, a framework layer920, a software layer930, and an application layer940.

In at least one embodiment, as shown inFIG.9, data center infrastructure layer910may include a resource orchestrator912, grouped computing resources914, and node computing resources (“node C.R.s”)916(1)-1016(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s916(1)-1016(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field programmable gate arrays (FPGAs), data processing units, graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s916(1)-1016(N) may be a server having one or more of above-mentioned computing resources.

In at least one embodiment, resource orchestrator912may configure or otherwise control one or more node C.R.s916(1)-1016(N) and/or grouped computing resources914. In at least one embodiment, resource orchestrator912may include a software design infrastructure (“SDI”) management entity for data center900. In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof.

In at least one embodiment, as shown inFIG.9, framework layer920includes a job scheduler922, a configuration manager924, a resource manager926and a distributed file system928. In at least one embodiment, framework layer920may include a framework to support software932of software layer930and/or one or more application(s)942of application layer940. In at least one embodiment, software932or application(s)942may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. In at least one embodiment, framework layer920may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system928for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler922may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center900. In at least one embodiment, configuration manager924may be capable of configuring different layers such as software layer930and framework layer920including Spark and distributed file system928for supporting large-scale data processing. In at least one embodiment, resource manager926may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system928and job scheduler922. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource914at data center infrastructure layer910. In at least one embodiment, resource manager926may coordinate with resource orchestrator912to manage these mapped or allocated computing resources.

In at least one embodiment, software932included in software layer930may include software used by at least portions of node C.R.s916(1)-1016(N), grouped computing resources914, and/or distributed file system928of framework layer920. The one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s)942included in application layer940may include one or more types of applications used by at least portions of node C.R.s916(1)-1016(N), grouped computing resources914, and/or distributed file system928of framework layer920. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager924, resource manager926, and resource orchestrator912may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center900from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

In at least one embodiment, data center900may include tools, services, software, or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center900. In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center900by using weight parameters calculated through one or more training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

Computer Systems

In at least one embodiment, computer system1000may include, without limitation, processor1002that may include, without limitation, one or more execution units1008to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system1000is a single processor desktop or server system, but in another embodiment computer system1000may be a multiprocessor system. In at least one embodiment, processor1002may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor1002may be coupled to a processor bus1010that may transmit data signals between processor1002and other components in computer system1000.

In at least one embodiment, processor1002may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”)1004. In at least one embodiment, processor1002may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor1002. Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file1006may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register.

In at least one embodiment, execution unit1008, including, without limitation, logic to perform integer and floating point operations, also resides in processor1002. In at least one embodiment, processor1002may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit1008may include logic to handle a packed instruction set1009. In at least one embodiment, by including packed instruction set1009in an instruction set of a general-purpose processor1002, along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor1002. In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor's data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor's data bus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit1008may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system1000may include, without limitation, a memory1020. In at least one embodiment, memory1020may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory1020may store instruction(s)1019and/or data1021represented by data signals that may be executed by processor1002.

In at least one embodiment, system logic chip may be coupled to processor bus1010and memory1020. In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”)1016, and processor1002may communicate with MCH1016via processor bus1010. In at least one embodiment, MCH1016may provide a high bandwidth memory path1018to memory1020for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH1016may direct data signals between processor1002, memory1020, and other components in computer system1000and to bridge data signals between processor bus1010, memory1020, and a system I/O1022. In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH1016may be coupled to memory1020through a high bandwidth memory path1018and graphics/video card1012may be coupled to MCH1016through an Accelerated Graphics Port (“AGP”) interconnect1014.

In at least one embodiment, computer system1000may use system I/O1022that is a proprietary hub interface bus to couple MCH1016to I/O controller hub (“ICH”)1030. In at least one embodiment, ICH1030may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory1020, chipset, and processor1002. Examples may include, without limitation, an audio controller1029, a firmware hub (“flash BIOS”)1028, a wireless transceiver1026, a data storage1024, a legacy I/O controller1023containing user input and keyboard interfaces1025, a serial expansion port1027, such as Universal Serial Bus (“USB”), and a network controller1034, which may include in some embodiments, a data processing unit. Data storage1024may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.

In at least one embodiment,FIG.10illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.10may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of computer system1000are interconnected using compute express link (CXL) interconnects.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.11is a block diagram illustrating an electronic device1100for utilizing a processor1110, according to at least one embodiment. In at least one embodiment, electronic device1100may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, an edge device, an IoT device, or any other suitable electronic device.

In at least one embodiment, system1100may include, without limitation, processor1110communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor1110coupled using a bus or interface, such as a 1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In at least one embodiment,FIG.11illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.11may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated inFIG.11may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components ofFIG.11are interconnected using compute express link (CXL) interconnects.

In at least one embodiment,FIG.11may include a display1124, a touch screen1125, a touch pad1130, a Near Field Communications unit (“NFC”)1145, a sensor hub1140, a thermal sensor1146, an Express Chipset (“EC”)1135, a Trusted Platform Module (“TPM”)1138, BIOS/firmware/flash memory (“BIOS, FW Flash”)1122, a DSP1160, a drive1120such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”)1150, a Bluetooth unit1152, a Wireless Wide Area Network unit (“WWAN”)1156, a Global Positioning System (GPS)1155, a camera (“USB 3.0 camera”)1154such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)1115implemented in, for example, LPDDR3 standard. These components may each be implemented in any suitable manner.

In at least one embodiment, other components may be communicatively coupled to processor1110through components discussed above. In at least one embodiment, an accelerometer1141, Ambient Light Sensor (“ALS”)1142, compass1143, and a gyroscope1144may be communicatively coupled to sensor hub1140. In at least one embodiment, thermal sensor1139, a fan1137, a keyboard1136, and a touch pad1130may be communicatively coupled to EC1135. In at least one embodiment, speaker1163, headphones1164, and microphone (“mic”)1165may be communicatively coupled to an audio unit (“audio codec and class d amp”)1162, which may in turn be communicatively coupled to DSP1160. In at least one embodiment, audio unit1164may include, for example and without limitation, an audio coder/decoder (“codec”) and a class D amplifier. In at least one embodiment, SIM card (“SIM”)1157may be communicatively coupled to WWAN unit1156. In at least one embodiment, components such as WLAN unit1150and Bluetooth unit1152, as well as WWAN unit1156may be implemented in a Next Generation Form Factor (“NGFF”).

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.12is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system1200includes one or more processors1202and one or more graphics processors1208, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors1202or processor cores1207. In at least one embodiment, system1200is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, edge, or embedded devices.

In at least one embodiment, system1200may include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system1200is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system1200may also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In at least one embodiment, processing system1200is a television or set top box device having one or more processors1202and a graphical interface generated by one or more graphics processors1208.

In at least one embodiment, one or more processors1202each include one or more processor cores1207to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores1207is configured to process a specific instruction set1209. In at least one embodiment, instruction set1209may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores1207may each process a different instruction set1209, which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core1207may also include other processing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor1202includes cache memory1204. In at least one embodiment, processor1202may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor1202. In at least one embodiment, processor1202also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores1207using known cache coherency techniques. In at least one embodiment, register file1206is additionally included in processor1202which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file1206may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s)1202are coupled with one or more interface bus(es)1210to transmit communication signals such as address, data, or control signals between processor1202and other components in system1200. In at least one embodiment, interface bus1210, in one embodiment, may be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface1210is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s)1202include an integrated memory controller1216and a platform controller hub1230. In at least one embodiment, memory controller1216facilitates communication between a memory device and other components of system1200, while platform controller hub (PCH)1230provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device1220may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device1220may operate as system memory for system1200, to store data1222and instructions1221for use when one or more processors1202executes an application or process. In at least one embodiment, memory controller1216also couples with an optional external graphics processor1212, which may communicate with one or more graphics processors1208in processors1202to perform graphics and media operations. In at least one embodiment, a display device1211may connect to processor(s)1202. In at least one embodiment display device1211may include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., Di splayPort, etc.). In at least one embodiment, display device1211may include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In at least one embodiment, platform controller hub1230enables peripherals to connect to memory device1220and processor1202via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller1246, a network controller1234, a firmware interface1228, a wireless transceiver1226, touch sensors1225, a data storage device1224(e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device1224may connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors1225may include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver1226may be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at least one embodiment, firmware interface1228enables communication with system firmware, and may be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller1234may enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus1210. In at least one embodiment, audio controller1246is a multi-channel high definition audio controller. In at least one embodiment, system1200includes an optional legacy I/O controller1240for coupling legacy (e.g., Personal System2(PS/2)) devices to system. In at least one embodiment, platform controller hub1230may also connect to one or more Universal Serial Bus (USB) controllers1242connect input devices, such as keyboard and mouse1243combinations, a camera1244, or other USB input devices.

In at least one embodiment, an instance of memory controller1216and platform controller hub1230may be integrated into a discreet external graphics processor, such as external graphics processor1212. In at least one embodiment, platform controller hub1230and/or memory controller1216may be external to one or more processor(s)1202. For example, in at least one embodiment, system1200may include an external memory controller1216and platform controller hub1230, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s)1202.

Inference and/or training logic815are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic815are provided below in conjunction withFIGS.8A and/or8B. In at least one embodiment portions or all of inference and/or training logic815may be incorporated into graphics processor1300. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in a graphics processor. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.8A or8B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of a graphics processor to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.13is a block diagram of a processor1300having one or more processor cores1302A-1402N, an integrated memory controller1314, and an integrated graphics processor1308, according to at least one embodiment. In at least one embodiment, processor1300may include additional cores up to and including additional core1302N represented by dashed lined boxes. In at least one embodiment, each of processor cores1302A-1402N includes one or more internal cache units1304A-1404N. In at least one embodiment, each processor core also has access to one or more shared cached units1306.

In at least one embodiment, internal cache units1304A-1404N and shared cache units1306represent a cache memory hierarchy within processor1300. In at least one embodiment, cache memory units1304A-1404N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units1306and1304A-1404N.

In at least one embodiment, processor1300may also include a set of one or more bus controller units1316and a system agent core1310. In at least one embodiment, one or more bus controller units1316manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core1310provides management functionality for various processor components. In at least one embodiment, system agent core1310includes one or more integrated memory controllers1314to manage access to various external memory devices (not shown).

In at least one embodiment, one or more of processor cores1302A-1402N include support for simultaneous multi-threading. In at least one embodiment, system agent core1310includes components for coordinating and operating cores1302A-1402N during multi-threaded processing. In at least one embodiment, system agent core1310may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores1302A-1402N and graphics processor1308.

In at least one embodiment, processor1300additionally includes graphics processor1308to execute graphics processing operations. In at least one embodiment, graphics processor1308couples with shared cache units1306, and system agent core1310, including one or more integrated memory controllers1314. In at least one embodiment, system agent core1310also includes a display controller1311to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller1311may also be a separate module coupled with graphics processor1308via at least one interconnect, or may be integrated within graphics processor1308.

In at least one embodiment, a ring based interconnect unit1312is used to couple internal components of processor1300. In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor1308couples with ring interconnect1312via an I/O link1313.

In at least one embodiment, I/O link1313represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module1318, such as an eDRAM module. In at least one embodiment, each of processor cores1302A-1402N and graphics processor1308use embedded memory modules1318as a shared Last Level Cache.

In at least one embodiment, processor cores1302A-1402N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores1302A-1402N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores1302A-1402N execute a common instruction set, while one or more other cores of processor cores1302A-1402N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores1302A-1402N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor1300may be implemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic815are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic815are provided below in conjunction withFIGS.8A and/or8B. In at least one embodiment portions or all of inference and/or training logic815may be incorporated into processor1300. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor1308, graphics core(s)1302A-1402N, or other components inFIG.13. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.8A or8B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor1300to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

Virtualized Computing Platform

FIG.14is an example data flow diagram for a process1400of generating and deploying an image processing and inferencing pipeline, in accordance with at least one embodiment. In at least one embodiment, process1400may be deployed for use with imaging devices, processing devices, and/or other device types at one or more facilities1402. Process1400may be executed within a training system1404and/or a deployment system1406. In at least one embodiment, training system1404may be used to perform training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system1406. In at least one embodiment, deployment system1406may be configured to offload processing and compute resources among a distributed computing environment to reduce infrastructure requirements at facility1402. In at least one embodiment, one or more applications in a pipeline may use or call upon services (e.g., inference, visualization, compute, AI, etc.) of deployment system1406during execution of applications.

In at least one embodiment, some of applications used in advanced processing and inferencing pipelines may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models may be trained at facility1402using data1408(such as imaging data) generated at facility1402(and stored on one or more picture archiving and communication system (PACS) servers at facility1402), may be trained using imaging or sequencing data1408from another facility(ies), or a combination thereof. In at least one embodiment, training system1404may be used to provide applications, services, and/or other resources for generating working, deployable machine learning models for deployment system1406.

In at least one embodiment, model registry1424may be backed by object storage that may support versioning and object metadata. In at least one embodiment, object storage may be accessible through, for example, a cloud storage (e.g., cloud1526ofFIG.15) compatible application programming interface (API) from within a cloud platform. In at least one embodiment, machine learning models within model registry1424may uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API may provide access to methods that allow users with appropriate credentials to associate models with applications, such that models may be executed as part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline1504(FIG.15) may include a scenario where facility1402is training their own machine learning model, or has an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data1408generated by imaging device(s), sequencing devices, and/or other device types may be received. In at least one embodiment, once imaging data1408is received, AI-assisted annotation1410may be used to aid in generating annotations corresponding to imaging data1408to be used as ground truth data for a machine learning model. In at least one embodiment, AI-assisted annotation1410may include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that may be trained to generate annotations corresponding to certain types of imaging data1408(e.g., from certain devices). In at least one embodiment, AI-assisted annotations1410may then be used directly, or may be adjusted or fine-tuned using an annotation tool to generate ground truth data. In at least one embodiment, AI-assisted annotations1410, labeled clinic data1412, or a combination thereof may be used as ground truth data for training a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1416, and may be used by deployment system1406, as described herein.

In at least one embodiment, training pipeline1504(FIG.15) may include a scenario where facility1402needs a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1406, but facility1402may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, an existing machine learning model may be selected from a model registry1424. In at least one embodiment, model registry1424may include machine learning models trained to perform a variety of different inference tasks on imaging data. In at least one embodiment, machine learning models in model registry1424may have been trained on imaging data from different facilities than facility1402(e.g., facilities remotely located). In at least one embodiment, machine learning models may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, when being trained on imaging data from a specific location, training may take place at that location, or at least in a manner that protects confidentiality of imaging data or restricts imaging data from being transferred off-premises. In at least one embodiment, once a model is trained—or partially trained—at one location, a machine learning model may be added to model registry1424. In at least one embodiment, a machine learning model may then be retrained, or updated, at any number of other facilities, and a retrained or updated model may be made available in model registry1424. In at least one embodiment, a machine learning model may then be selected from model registry1424—and referred to as output model1416—and may be used in deployment system1406to perform one or more processing tasks for one or more applications of a deployment system.

In at least one embodiment, training pipeline1504(FIG.15), a scenario may include facility1402requiring a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1406, but facility1402may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, a machine learning model selected from model registry1424may not be fine-tuned or optimized for imaging data1408generated at facility1402because of differences in populations, robustness of training data used to train a machine learning model, diversity in anomalies of training data, and/or other issues with training data. In at least one embodiment, AI-assisted annotation1410may be used to aid in generating annotations corresponding to imaging data1408to be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data1412may be used as ground truth data for training a machine learning model. In at least one embodiment, retraining or updating a machine learning model may be referred to as model training1414. In at least one embodiment, model training1414—e.g., AI-assisted annotations1410, labeled clinic data1412, or a combination thereof—may be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1416, and may be used by deployment system1406, as described herein.

In at least one embodiment, deployment system1406may include software1418, services1420, hardware1422, and/or other components, features, and functionality. In at least one embodiment, deployment system1406may include a software “stack,” such that software1418may be built on top of services1420and may use services1420to perform some or all of processing tasks, and services1420and software1418may be built on top of hardware1422and use hardware1422to execute processing, storage, and/or other compute tasks of deployment system1406. In at least one embodiment, software1418may include any number of different containers, where each container may execute an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data1408, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility1402after processing through a pipeline (e.g., to convert outputs back to a usable data type). In at least one embodiment, a combination of containers within software1418(e.g., that make up a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may leverage services1420and hardware1422to execute some or all processing tasks of applications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data1408) in a specific format in response to an inference request (e.g., a request from a user of deployment system1406). In at least one embodiment, input data may be representative of one or more images, video, and/or other data representations generated by one or more imaging devices. In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and/or to prepare output data for transmission and/or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models1416of training system1404.

In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represents a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry1424and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user's system.

In at least one embodiment, developers (e.g., software developers, clinicians, doctors, etc.) may develop, publish, and store applications (e.g., as containers) for performing image processing and/or inferencing on supplied data. In at least one embodiment, development, publishing, and/or storing may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that an application and/or container developed is compliant with or compatible with a system). In at least one embodiment, an application that is developed may be tested locally (e.g., at a first facility, on data from a first facility) with an SDK which may support at least some of services1420as a system (e.g., system1500ofFIG.15). In at least one embodiment, because DICOM objects may contain anywhere from one to hundreds of images or other data types, and due to a variation in data, a developer may be responsible for managing (e.g., setting constructs for, building pre-processing into an application, etc.) extraction and preparation of incoming data. In at least one embodiment, once validated by system1500(e.g., for accuracy), an application may be available in a container registry for selection and/or implementation by a user to perform one or more processing tasks with respect to data at a facility (e.g., a second facility) of a user.

In at least one embodiment, developers may then share applications or containers through a network for access and use by users of a system (e.g., system1500ofFIG.15). In at least one embodiment, completed and validated applications or containers may be stored in a container registry and associated machine learning models may be stored in model registry1424. In at least one embodiment, a requesting entity—who provides an inference or image processing request—may browse a container registry and/or model registry1424for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in data processing pipeline, and submit an imaging processing request. In at least one embodiment, a request may include input data (and associated patient data, in some examples) that is necessary to perform a request, and/or may include a selection of application(s) and/or machine learning models to be executed in processing a request. In at least one embodiment, a request may then be passed to one or more components of deployment system1406(e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system1406may include referencing selected elements (e.g., applications, containers, models, etc.) from a container registry and/or model registry1424. In at least one embodiment, once results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a local, on-premises workstation or terminal).

In at least one embodiment, to aid in processing or execution of applications or containers in pipelines, services1420may be leveraged. In at least one embodiment, services1420may include compute services, artificial intelligence (AI) services, visualization services, and/or other service types. In at least one embodiment, services1420may provide functionality that is common to one or more applications in software1418, so functionality may be abstracted to a service that may be called upon or leveraged by applications. In at least one embodiment, functionality provided by services1420may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform1530(FIG.15)). In at least one embodiment, rather than each application that shares a same functionality offered by a service1420being required to have a respective instance of service1420, service1420may be shared between and among various applications. In at least one embodiment, services may include an inference server or engine that may be used for executing detection or segmentation tasks, as non-limiting examples. In at least one embodiment, a model training service may be included that may provide machine learning model training and/or retraining capabilities. In at least one embodiment, a data augmentation service may further be included that may provide GPU accelerated data (e.g., DICOM, RIS, CIS, REST compliant, RPC, raw, etc.) extraction, resizing, scaling, and/or other augmentation. In at least one embodiment, a visualization service may be used that may add image rendering effects—such as ray-tracing, rasterization, denoising, sharpening, etc.—to add realism to two-dimensional (2D) and/or three-dimensional (3D) models. In at least one embodiment, virtual instrument services may be included that provide for beam-forming, segmentation, inferencing, imaging, and/or support for other applications within pipelines of virtual instruments.

In at least one embodiment, where a service1420includes an AI service (e.g., an inference service), one or more machine learning models may be executed by calling upon (e.g., as an API call) an inference service (e.g., an inference server) to execute machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, an application may call upon an inference service to execute machine learning models for performing one or more of processing operations associated with segmentation tasks. In at least one embodiment, software1418implementing advanced processing and inferencing pipeline that includes segmentation application and anomaly detection application may be streamlined because each application may call upon a same inference service to perform one or more inferencing tasks.

In at least one embodiment, hardware1422may include GPUs, CPUs, DPUs, graphics cards, an AI/deep learning system (e.g., an AI supercomputer, such as NVIDIA's DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware1422may be used to provide efficient, purpose-built support for software1418and services1420in deployment system1406. In at least one embodiment, use of GPU processing may be implemented for processing locally (e.g., at facility1402), within an AI/deep learning system, in a cloud system, and/or in other processing components of deployment system1406to improve efficiency, accuracy, and efficacy of image processing and generation. In at least one embodiment, software1418and/or services1420may be optimized for GPU processing with respect to deep learning, machine learning, and/or high-performance computing, as non-limiting examples. In at least one embodiment, at least some of computing environment of deployment system1406and/or training system1404may be executed in a datacenter one or more supercomputers or high performance computing systems, with GPU optimized software (e.g., hardware and software combination of NVIDIA's DGX System). In at least one embodiment, hardware1422may include any number of GPUs that may be called upon to perform processing of data in parallel, as described herein. In at least one embodiment, cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform may further include DPU processing to transmit data received over a network and/or through a network controller or other network interface directly to (e.g., a memory of) one or more GPU(s). In at least one embodiment, cloud platform (e.g., NVIDIA's NGC) may be executed using an AI/deep learning supercomputer(s) and/or GPU-optimized software (e.g., as provided on NVIDIA's DGX Systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing.

FIG.15is a system diagram for an example system1500for generating and deploying an imaging deployment pipeline, in accordance with at least one embodiment. In at least one embodiment, system1500may be used to implement process1400ofFIG.14and/or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system1500may include training system1404and deployment system1406. In at least one embodiment, training system1404and deployment system1406may be implemented using software1418, services1420, and/or hardware1422, as described herein.

In at least one embodiment, system1500(e.g., training system1404and/or deployment system1406) may implemented in a cloud computing environment (e.g., using cloud1526). In at least one embodiment, system1500may be implemented locally with respect to a healthcare services facility, or as a combination of both cloud and local computing resources. In at least one embodiment, access to APIs in cloud1526may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system1500, may be restricted to a set of public IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system1500may communicate between and among one another using any of a variety of different network types, including but not limited to local area networks (LANs) and/or wide area networks (WANs) via wired and/or wireless communication protocols. In at least one embodiment, communication between facilities and components of system1500(e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated over data bus(ses), wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system1404may execute training pipelines1504, similar to those described herein with respect toFIG.14. In at least one embodiment, where one or more machine learning models are to be used in deployment pipelines1510by deployment system1406, training pipelines1504may be used to train or retrain one or more (e.g. pre-trained) models, and/or implement one or more of pre-trained models1506(e.g., without a need for retraining or updating). In at least one embodiment, as a result of training pipelines1504, output model(s)1416may be generated. In at least one embodiment, training pipelines1504may include any number of processing steps, such as but not limited to imaging data (or other input data) conversion or adaption In at least one embodiment, for different machine learning models used by deployment system1406, different training pipelines1504may be used. In at least one embodiment, training pipeline1504similar to a first example described with respect toFIG.14may be used for a first machine learning model, training pipeline1504similar to a second example described with respect toFIG.14may be used for a second machine learning model, and training pipeline1504similar to a third example described with respect toFIG.14may be used for a third machine learning model. In at least one embodiment, any combination of tasks within training system1404may be used depending on what is required for each respective machine learning model. In at least one embodiment, one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system1404, and may be implemented by deployment system1406.

In at least one embodiment, output model(s)1416and/or pre-trained model(s)1506may include any types of machine learning models depending on implementation or embodiment. In at least one embodiment, and without limitation, machine learning models used by system1500may include machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), K means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional, generative adversarial, liquid state machine, etc.), and/or other types of machine learning models.

In at least one embodiment, training pipelines1504may include AI-assisted annotation, as described in more detail herein with respect to at leastFIG.16B. In at least one embodiment, labeled data1412(e.g., traditional annotation) may be generated by any number of techniques. In at least one embodiment, labels or other annotations may be generated within a drawing program (e.g., an annotation program), a computer aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and/or may be hand drawn, in some examples. In at least one embodiment, ground truth data may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines location of labels), and/or a combination thereof. In at least one embodiment, for each instance of imaging data1408(or other data type used by machine learning models), there may be corresponding ground truth data generated by training system1404. In at least one embodiment, AI-assisted annotation may be performed as part of deployment pipelines1510; either in addition to, or in lieu of AI-assisted annotation included in training pipelines1504. In at least one embodiment, system1500may include a multi-layer platform that may include a software layer (e.g., software1418) of diagnostic applications (or other application types) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, system1500may be communicatively coupled to (e.g., via encrypted links) PACS server networks of one or more facilities. In at least one embodiment, system1500may be configured to access and referenced data from PACS servers to perform operations, such as training machine learning models, deploying machine learning models, image processing, inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as a secure, encrypted, and/or authenticated API through which applications or containers may be invoked (e.g., called) from an external environment(s) (e.g., facility1402). In at least one embodiment, applications may then call or execute one or more services1420for performing compute, AI, or visualization tasks associated with respective applications, and software1418and/or services1420may leverage hardware1422to perform processing tasks in an effective and efficient manner.

In at least one embodiment, deployment system1406may execute deployment pipelines1510. In at least one embodiment, deployment pipelines1510may include any number of applications that may be sequentially, non-sequentially, or otherwise applied to imaging data (and/or other data types) generated by imaging devices, sequencing devices, genomics devices, etc.—including AI-assisted annotation, as described above. In at least one embodiment, as described herein, a deployment pipeline1510for an individual device may be referred to as a virtual instrument for a device (e.g., a virtual ultrasound instrument, a virtual CT scan instrument, a virtual sequencing instrument, etc.). In at least one embodiment, for a single device, there may be more than one deployment pipeline1510depending on information desired from data generated by a device. In at least one embodiment, where detections of anomalies are desired from an MRI machine, there may be a first deployment pipeline1510, and where image enhancement is desired from output of an Mill machine, there may be a second deployment pipeline1510.

In at least one embodiment, an image generation application may include a processing task that includes use of a machine learning model. In at least one embodiment, a user may desire to use their own machine learning model, or to select a machine learning model from model registry1424. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model for inclusion in an application for performing a processing task. In at least one embodiment, applications may be selectable and customizable, and by defining constructs of applications, deployment, and implementation of applications for a particular user are presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system1500—such as services1420and hardware1422—deployment pipelines1510may be even more user friendly, provide for easier integration, and produce more accurate, efficient, and timely results.

In at least one embodiment, deployment system1406may include a user interface1514(e.g., a graphical user interface, a web interface, etc.) that may be used to select applications for inclusion in deployment pipeline(s)1510, arrange applications, modify, or change applications or parameters or constructs thereof, use and interact with deployment pipeline(s)1510during set-up and/or deployment, and/or to otherwise interact with deployment system1406. In at least one embodiment, although not illustrated with respect to training system1404, user interface1514(or a different user interface) may be used for selecting models for use in deployment system1406, for selecting models for training, or retraining, in training system1404, and/or for otherwise interacting with training system1404.

In at least one embodiment, pipeline manager1512may be used, in addition to an application orchestration system1528, to manage interaction between applications or containers of deployment pipeline(s)1510and services1420and/or hardware1422. In at least one embodiment, pipeline manager1512may be configured to facilitate interactions from application to application, from application to service1420, and/or from application or service to hardware1422. In at least one embodiment, although illustrated as included in software1418, this is not intended to be limiting, and in some examples (e.g., as illustrated inFIG.13) pipeline manager1512may be included in services1420. In at least one embodiment, application orchestration system1528(e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from deployment pipeline(s)1510(e.g., a reconstruction application, a segmentation application, etc.) with individual containers, each application may execute in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency.

In at least one embodiment, each application and/or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow for focus on, and attention to, a task of a single application and/or container(s) without being hindered by tasks of another application(s) or container(s). In at least one embodiment, communication, and cooperation between different containers or applications may be aided by pipeline manager1512and application orchestration system1528. In at least one embodiment, so long as an expected input and/or output of each container or application is known by a system (e.g., based on constructs of applications or containers), application orchestration system1528and/or pipeline manager1512may facilitate communication among and between, and sharing of resources among and between, each of applications or containers. In at least one embodiment, because one or more of applications or containers in deployment pipeline(s)1510may share same services and resources, application orchestration system1528may orchestrate, load balance, and determine sharing of services or resources between and among various applications or containers. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability. In at least one embodiment, a scheduler may thus allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and/or other component of application orchestration system1528) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of need for data outputs (e.g., to determine whether to execute real-time processing or delayed processing), etc.

In at least one embodiment, services1420leveraged by and shared by applications or containers in deployment system1406may include compute services1516, AI services1518, visualization services1520, and/or other service types. In at least one embodiment, applications may call (e.g., execute) one or more of services1420to perform processing operations for an application. In at least one embodiment, compute services1516may be leveraged by applications to perform super-computing or other high-performance computing (HPC) tasks. In at least one embodiment, compute service(s)1516may be leveraged to perform parallel processing (e.g., using a parallel computing platform1530) for processing data through one or more of applications and/or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform1530(e.g., NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU) (e.g., GPUs1522). In at least one embodiment, a software layer of parallel computing platform1530may provide access to virtual instruction sets and parallel computational elements of GPUs, for execution of compute kernels. In at least one embodiment, parallel computing platform1530may include memory and, in some embodiments, a memory may be shared between and among multiple containers, and/or between and among different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and/or for multiple processes within a container to use same data from a shared segment of memory of parallel computing platform1530(e.g., where multiple different stages of an application or multiple applications are processing same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read/write operation), same data in same location of a memory may be used for any number of processing tasks (e.g., at a same time, at different times, etc.). In at least one embodiment, as data is used to generate new data as a result of processing, this information of a new location of data may be stored and shared between various applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how a payload is understood within containers.

In at least one embodiment, AI services1518may be leveraged to perform inferencing services for executing machine learning model(s) associated with applications (e.g., tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services1518may leverage AI system1524to execute machine learning model(s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline(s)1510may use one or more of output models1416from training system1404and/or other models of applications to perform inference on imaging data. In at least one embodiment, two or more examples of inferencing using application orchestration system1528(e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority/low latency path that may achieve higher service level agreements, such as for performing inference on urgent requests during an emergency, or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may be non-urgent or where analysis may be performed at a later time. In at least one embodiment, application orchestration system1528may distribute resources (e.g., services1420and/or hardware1422) based on priority paths for different inferencing tasks of AI services1518.

In at least one embodiment, shared storage may be mounted to AI services1518within system1500. In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a request may be received by a set of API instances of deployment system1406, and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry1424if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache. In at least one embodiment, a scheduler (e.g., of pipeline manager1512) may be used to launch an application that is referenced in a request if an application is not already running or if there are not enough instances of an application. In at least one embodiment, if an inference server is not already launched to execute a model, an inference server may be launched. Any number of inference servers may be launched per model. In at least one embodiment, in a pull model, in which inference servers are clustered, models may be cached whenever load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding, distributed servers.

In at least one embodiment, inferencing may be performed using an inference server that runs in a container. In at least one embodiment, an instance of an inference server may be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request to perform inference on a model is received, a new instance may be loaded. In at least one embodiment, when starting an inference server, a model may be passed to an inference server such that a same container may be used to serve different models so long as inference server is running as a different instance.

In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., hosting an instance of an inference server) may be loaded (if not already), and a start procedure may be called. In at least one embodiment, pre-processing logic in a container may load, decode, and/or perform any additional pre-processing on incoming data (e.g., using a CPU(s) and/or GPU(s) and/or DPU(s)). In at least one embodiment, once data is prepared for inference, a container may perform inference as necessary on data. In at least one embodiment, this may include a single inference call on one image (e.g., a hand X-ray), or may require inference on hundreds of images (e.g., a chest CT). In at least one embodiment, an application may summarize results before completing, which may include, without limitation, a single confidence score, pixel level-segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings. In at least one embodiment, different models or applications may be assigned different priorities. For example, some models may have a real-time (TAT<1 min) priority while others may have lower priority (e.g., TAT<11 min). In at least one embodiment, model execution times may be measured from requesting institution or entity and may include partner network traversal time, as well as execution on an inference service.

In at least one embodiment, transfer of requests between services1420and inference applications may be hidden behind a software development kit (SDK), and robust transport may be provided through a queue. In at least one embodiment, a request will be placed in a queue via an API for an individual application/tenant ID combination and an SDK will pull a request from a queue and give a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from where an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as it may allow any instance of an application to pick up work as it becomes available. Results may be transferred back through a queue, to ensure no data is lost. In at least one embodiment, queues may also provide an ability to segment work, as highest priority work may go to a queue with most instances of an application connected to it, while lowest priority work may go to a queue with a single instance connected to it that processes tasks in an order received. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud1526, and an inference service may perform inferencing on a GPU.

In at least one embodiment, visualization services1520may be leveraged to generate visualizations for viewing outputs of applications and/or deployment pipeline(s)1510. In at least one embodiment, GPUs1522may be leveraged by visualization services1520to generate visualizations. In at least one embodiment, rendering effects, such as ray-tracing, may be implemented by visualization services1520to generate higher quality visualizations. In at least one embodiment, visualizations may include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services1520may include an internal visualizer, cinematics, and/or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.).

In at least one embodiment, hardware1422may include GPUs1522, AI system1524, cloud1526, and/or any other hardware used for executing training system1404and/or deployment system1406. In at least one embodiment, GPUs1522(e.g., NVIDIA's TESLA and/or QUADRO GPUs) may include any number of GPUs that may be used for executing processing tasks of compute services1516, AI services1518, visualization services1520, other services, and/or any of features or functionality of software1418. For example, with respect to AI services1518, GPUs1522may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on outputs of machine learning models, and/or to perform inferencing (e.g., to execute machine learning models). In at least one embodiment, cloud1526, AI system1524, and/or other components of system1500may use GPUs1522. In at least one embodiment, cloud1526may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system1524may use GPUs, and cloud1526—or at least a portion tasked with deep learning or inferencing—may be executed using one or more AI systems1524. As such, although hardware1422is illustrated as discrete components, this is not intended to be limiting, and any components of hardware1422may be combined with, or leveraged by, any other components of hardware1422.

In at least one embodiment, AI system1524may include a purpose-built computing system (e.g., a super-computer or an HPC) configured for inferencing, deep learning, machine learning, and/or other artificial intelligence tasks. In at least one embodiment, AI system1524(e.g., NVIDIA's DGX) may include GPU-optimized software (e.g., a software stack) that may be executed using a plurality of GPUs1522, in addition to DPUs, CPUs, RAM, storage, and/or other components, features, or functionality. In at least one embodiment, one or more AI systems1524may be implemented in cloud1526(e.g., in a data center) for performing some or all of AI-based processing tasks of system1500.

In at least one embodiment, cloud1526may include a GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimized platform for executing processing tasks of system1500. In at least one embodiment, cloud1526may include an AI system(s)1524for performing one or more of AI-based tasks of system1500(e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud1526may integrate with application orchestration system1528leveraging multiple GPUs to enable seamless scaling and load balancing between and among applications and services1420. In at least one embodiment, cloud1526may tasked with executing at least some of services1420of system1500, including compute services1516, AI services1518, and/or visualization services1520, as described herein. In at least one embodiment, cloud1526may perform small and large batch inference (e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallel computing API and platform1530(e.g., NVIDIA's CUDA), execute application orchestration system1528(e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray-tracing, 2D graphics, 3D graphics, and/or other rendering techniques to produce higher quality cinematics), and/or may provide other functionality for system1500.

FIG.16Aillustrates a data flow diagram for a process1600to train, retrain, or update a machine learning model, in accordance with at least one embodiment. In at least one embodiment, process1600may be executed using, as a non-limiting example, system1500ofFIG.15. In at least one embodiment, process1600may leverage services1420and/or hardware1422of system1500, as described herein. In at least one embodiment, refined models1612generated by process1600may be executed by deployment system1406for one or more containerized applications in deployment pipelines1510.

In at least one embodiment, model training1414may include retraining or updating an initial model1604(e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset1606, and/or new ground truth data associated with input data). In at least one embodiment, to retrain, or update, initial model1604, output or loss layer(s) of initial model1604may be reset, or deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model1604may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining1414may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training1414, by having reset or replaced output or loss layer(s) of initial model1604, parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset1606(e.g., image data1408ofFIG.14).

In at least one embodiment, pre-trained models1506may be stored in a data store, or registry (e.g., model registry1424ofFIG.14). In at least one embodiment, pre-trained models1506may have been trained, at least in part, at one or more facilities other than a facility executing process1600. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities, pre-trained models1506may have been trained, on-premise, using customer or patient data generated on-premise. In at least one embodiment, pre-trained models1506may be trained using cloud1526and/or other hardware1422, but confidential, privacy protected patient data may not be transferred to, used by, or accessible to any components of cloud1526(or other off premise hardware). In at least one embodiment, where a pre-trained model1506is trained at using patient data from more than one facility, pre-trained model1506may have been individually trained for each facility prior to being trained on patient or customer data from another facility. In at least one embodiment, such as where a customer or patient data has been released of privacy concerns (e.g., by waiver, for experimental use, etc.), or where a customer or patient data is included in a public data set, a customer or patient data from any number of facilities may be used to train pre-trained model1506on-premise and/or off premise, such as in a datacenter or other cloud computing infrastructure.

In at least one embodiment, when selecting applications for use in deployment pipelines1510, a user may also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model for use, so a user may select a pre-trained model1506to use with an application. In at least one embodiment, pre-trained model1506may not be optimized for generating accurate results on customer dataset1606of a facility of a user (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.). In at least one embodiment, prior to deploying pre-trained model1506into deployment pipeline1510for use with an application(s), pre-trained model1506may be updated, retrained, and/or fine-tuned for use at a respective facility.

In at least one embodiment, a user may select pre-trained model1506that is to be updated, retrained, and/or fine-tuned, and pre-trained model1506may be referred to as initial model1604for training system1404within process1600. In at least one embodiment, customer dataset1606(e.g., imaging data, genomics data, sequencing data, or other data types generated by devices at a facility) may be used to perform model training1414(which may include, without limitation, transfer learning) on initial model1604to generate refined model1612. In at least one embodiment, ground truth data corresponding to customer dataset1606may be generated by training system1404. In at least one embodiment, ground truth data may be generated, at least in part, by clinicians, scientists, doctors, practitioners, at a facility (e.g., as labeled clinic data1412ofFIG.14).

In at least one embodiment, AI-assisted annotation1410may be used in some examples to generate ground truth data. In at least one embodiment, AI-assisted annotation1410(e.g., implemented using an AI-assisted annotation SDK) may leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground truth data for a customer dataset. In at least one embodiment, user1610may use annotation tools within a user interface (a graphical user interface (GUI)) on computing device1608.

In at least one embodiment, user1610may interact with a GUI via computing device1608to edit or fine-tune (auto)annotations. In at least one embodiment, a polygon editing feature may be used to move vertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset1606has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training1414to generate refined model1612. In at least one embodiment, customer dataset1606may be applied to initial model1604any number of times, and ground truth data may be used to update parameters of initial model1604until an acceptable level of accuracy is attained for refined model1612. In at least one embodiment, once refined model1612is generated, refined model1612may be deployed within one or more deployment pipelines1510at a facility for performing one or more processing tasks with respect to medical imaging data.

In at least one embodiment, refined model1612may be uploaded to pre-trained models1506in model registry1424to be selected by another facility. In at least one embodiment, his process may be completed at any number of facilities such that refined model1612may be further refined on new datasets any number of times to generate a more universal model.

FIG.16Bis an example illustration of a client-server architecture1632to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. In at least one embodiment, AI-assisted annotation tools1636may be instantiated based on a client-server architecture1632. In at least one embodiment, annotation tools1636in imaging applications may aid radiologists, for example, identify organs and abnormalities. In at least one embodiment, imaging applications may include software tools that help user1610to identify, as a non-limiting example, a few extreme points on a particular organ of interest in raw images1634(e.g., in a 3D MRI or CT scan) and receive auto-annotated results for all 2D slices of a particular organ. In at least one embodiment, results may be stored in a data store as training data1638and used as (for example and without limitation) ground truth data for training. In at least one embodiment, when computing device1608sends extreme points for AI-assisted annotation1410, a deep learning model, for example, may receive this data as input and return inference results of a segmented organ or abnormality. In at least one embodiment, pre-instantiated annotation tools, such as AI-Assisted Annotation Tool1636B inFIG.16B, may be enhanced by making API calls (e.g., API Call1644) to a server, such as an Annotation Assistant Server1640that may include a set of pre-trained models1642stored in an annotation model registry, for example. In at least one embodiment, an annotation model registry may store pre-trained models1642(e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation on a particular organ or abnormality. These models may be further updated by using training pipelines1504. In at least one embodiment, pre-installed annotation tools may be improved over time as new labeled clinic data1412is added.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.