Patent ID: 12254679

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The following detailed description of example implementations refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements.

Utilizing machine learning models for computer vision applications requires large quantities of data for training, testing, and validating the machine learning models in order to obtain levels of recognition accuracy that are acceptable. Machine learning models may achieve higher accuracy if high-quality annotation data is used as part of a supervised learning process for training the machine learning models. Annotation involves marking and labeling objects appearing in images and videos used to do the training. In most cases, such annotation data is manually generated by humans due to unavailability of adequate tools to automate the process. As an example, there is a lack of tooling to do automated annotation of three-dimensional (3D) objects in captured video data that is inherently two-dimensional (2D). This slow and expensive manual process creates a bottleneck for developing and testing machine learning models, such as deep learning models. For example, manual annotation can slow the speed at which models can be deployed to perform computer vision applications, and manual annotators can introduce errors in the annotation data (human error). Thus, current techniques for generating annotation data for machine learning models are inefficient in their use of resources such as computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, human resources and/or other resources, and can be associated with generating poor-quality annotation data, generating insufficient machine learning models based on the poor-quality annotation data, incorrect predictions generated by the insufficient machine learning models, and/or the like.

Some implementations described herein provide an annotation system that generates three-dimensional annotations for training a machine learning model. For example, the annotation system may receive a video and corresponding camera information associated with a camera that captured the video, and may select an object in the video and a wire model for the object. The annotation system may adjust one or more of an orientation, a location, or a size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model. The annotation system may identify the object in another frame of the video, and may align the adjusted wire model on the object in the other frame. The annotation system may interpolate the adjusted wire model for the object for intermediate frames of the video between the frame and the other frame, and may generate three-dimensional (3D) annotations for the video based on the adjusted wire models for the frame, the intermediate frames, and the other frame. The annotation system may train a machine learning model based on the 3D annotations.

In this way, the annotation system generates three-dimensional annotations for training a machine learning model. For example, the annotation system may create high-quality annotation data (e.g., annotating three-dimensional objects in two-dimensional video data) for the machine learning models in a timely, accurate and inexpensive manner. The high-quality annotation data may be utilized to train machine learning models (e.g., neural network models) to better identify and estimate three-dimensional properties of detected objects (e.g., types, sizes, speeds, real world locations, and/or the like). Thus, the annotation system may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed, and avoid generating poor-quality annotation data, generating insufficient machine learning models based on the poor-quality annotation data, incorrect predictions generated by the insufficient machine learning models, and/or the like.

FIGS.1A-1Hare diagrams of an example100associated with generating three-dimensional annotations for training a machine learning model. As shown inFIGS.1A-1H, example100includes an annotation system105, an annotation data structure110, a camera information data structure, and a video data structure. Further details of the annotation system105, the annotation data structure110, the camera information data structure, and the video data structure are provided elsewhere herein.

As shown inFIG.1A, and by reference number115, the annotation system105may receive camera information identifying calibration and registration information for a plurality of cameras used to generate a plurality of videos. For example, the camera information data structure may store the camera information identifying the calibration and registration information for the plurality of cameras. The calibration information for the plurality of cameras may include information identifying intrinsic camera parameters (e.g., resolution, lens distortion, and/or the like) associated with the plurality of cameras. The registration information for the plurality of cameras may include information identifying extrinsic camera parameters (e.g., camera location, camera orientation, camera height, and/or the like) associated with the plurality of cameras. The camera information may also include camera identifiers associated with the plurality of cameras, stereo-imaging associated with the plurality of cameras, light detection and ranging (LIDAR) locations associated with the plurality of cameras, and/or the like.

The annotation system105may periodically receive the camera information from the camera information data structure, may continuously receive the camera information from the camera information data structure, may receive the camera information based on providing a request for the camera information to the camera information data structure, and/or the like.

As further shown inFIG.1A, and by reference number120, the annotation system105may receive the plurality of videos generated by the plurality of cameras. For example, the video data structure may receive and store the plurality of videos generated by the plurality of cameras. Each of the plurality of videos may include a two-dimensional representation of a scene captured by a corresponding one of the plurality of cameras over a time period. Each of the plurality of cameras may capture one or more videos of a scene over a time period and may provide the captured videos to the video data structure for storage. For example, a camera may capture a first video of a roadway for one hour and may provide the first video to the video data structure. The camera may capture a second video of the roadway for a subsequent hour and may provide the second video to the video data structure. Thus, the camera may capture and store twenty-four videos per day in the video data structure.

The annotation system105may periodically receive one or more of the plurality of videos from the video data structure, may continuously receive one or more of the plurality of videos from the video data structure, may receive the one or more of the plurality of videos based on providing a request for the one or more of the plurality of videos to the video data structure, and/or the like.

As further shown inFIG.1A, and by reference number125, the annotation system105may map, in the annotation data structure, the camera information with corresponding videos based on camera identifiers associated with the plurality of cameras. For example, the camera information may include the camera identifiers associated with the plurality of cameras, and each of the plurality of videos may be associated with a camera identifier. Thus, the annotation system105may map the camera information with corresponding videos based on the camera identifiers associated with the plurality of cameras. The annotation system105may store the camera information, the plurality of videos, and the mapping of the camera information with corresponding videos in the annotation data structure110. The annotation data structure110may include a database, a table, a list, and/or the like.

The camera information may enable the annotation system105to transform the two-dimensional representations of the plurality of videos into three dimensional representations by transforming objects in a two-dimensional image of a video into one or more three-dimensional points (e.g., an x, y, z point). The camera information may also enable the annotation system105to transform the three-dimensional points back to two dimensions. Thus, the annotation system105may annotate three-dimensional objects in the two-dimensional videos more easily and accurately than current systems. In some implementations, the annotation system105may utilize the camera information to obtain the third dimension to be used for all video generated by the camera. For example, the camera information may include a location of the camera capturing the video, a height of the camera from a ground plane, a distance of the camera from images captured in the video, an angle of the cameras relative to the ground plane, and/or the like. The annotation system105may utilize such information to obtain the third dimension to be used for all video generated by the camera.

As shown inFIG.1B, and by reference number130, the annotation system105may receive a video and corresponding camera information from the annotation data structure110. For example, the annotation system105may provide a request for a video to the annotation data structure110. The request may include information identifying the video, a camera identifier of one of the plurality of cameras, and/or the like. In some implementations, the request may include a request for any of the plurality of videos stored in the annotation data structure110. The annotation data structure110may retrieve the video based on the request and may retrieve the corresponding camera information based on the mapping of the camera information with corresponding videos. The annotation data structure110may provide the video and the corresponding camera information to the annotation system105, and the annotation system105may receive the video and the corresponding camera information from the annotation data structure110. The corresponding camera information may include the camera information associated with the camera that captured the video.

As further shown inFIG.1B, and by reference number135, the annotation system105may select an object in the video and a wire model (e.g., a three-dimensional segmentation polygon) for the object. For example, the annotation system105may analyze one or more frames of the video (e.g., via a computer vision model) and may identify objects in the one or more frames of the video. The annotation system105may select the object from the objects identified in the one or more frames of the video. The object may appear in a frame of the video that may not correspond to an ultimate first frame of the video (e.g., since the object may first appear later in the video). For example, the annotation system105may analyze a frame of the video and may select a vehicle on a roadway depicted in the frame as the object.

The annotation system105may include a predefined list of wire models for different objects that may appear in a video. The annotation system105may analyze the predefined list of wire models based on the selected object, and may select the wire model from the predefined list of wire models based on analyzing the predefined list. For example, if the annotation system105selects a vehicle as the object, the annotation system105may review wire models representing different types of vehicles (e.g., included in the predefined list). The annotation system105may select the wire model (e.g., the vehicle type) that most closely represents the selected vehicle (e.g., a sedan, a truck, a van, and/or the like). In some implementations, a user of the annotation system105may manually assign a wire model to an object in the frame, for example, in cases when the annotation system105has not detected the object or has mischaracterized the object for purposes of selecting the correct wire model.

As shown inFIG.1C, and by reference number140, the annotation system105may align the wire model on the object in a frame of the video. For example, the annotation system105may automatically insert or overlay the wire model on the object in the frame of the video. The annotation system105may overlay the wire model on a location in the frame of the video where the object appears. As shown, the object (e.g., the vehicle) may be located to the left in the frame of the video, and the annotation system105may align the wire model (e.g., a model representing a vehicle) on the location of the object in the frame of the video. An orientation and/or a size of the wire model may not be exactly the same as an orientation and/or a size of the object in the frame. The annotation system105may adjust one or more of an orientation, a location, and/or a size of the wire model on the object to align the wire model on the object in the frame of the video.

As shown inFIG.1D, and by reference number145, the annotation system105may adjust an orientation, a location, and/or a size of the wire model on the object based on the corresponding camera information. For example, since the orientation and/or the size of the wire model may not be exactly the same as the orientation and/or the size of the object in the frame, the annotation system105may adjust one or more of the orientation, the location, and/or the size of the model on the object based on the corresponding camera information. The corresponding camera information may include information identifying a location of the camera, an orientation of the camera, a height of the camera, a LIDAR location of the camera, and/or the like. Thus, the annotation system105may determine the orientation and/or the size of the object in the frame based on the corresponding camera information. The annotation system105may utilize the orientation and/or the size of the object in the frame to adjust the orientation, the location, and/or the size of the wire model, to generate an adjusted wire model. The annotation system105may overlay the adjusted wire model on the location in the frame of the video where the object appears.

As shown inFIG.1E, and by reference number150, the annotation system105may identify the object in another frame of the video and align the adjusted wire model on the object in the frame. For example, the annotation system105may analyze the video to determine another frame of the video where the object appears in the video. The other frame may be a frame occurring temporally after the frame described above with respect toFIGS.1C-1D. In that context, the frame described above may be considered a first analyzed frame of a video and the other frame may be considered a second analyzed frame of a video. In some embodiments, the other frame may not be a final frame of the video, but may include a frame of the video that occurs before the final frame (e.g., since the object may not appear in the final frame of the video being annotated). The annotation system105may analyze the other frame of the video (e.g., via a computer vision model), and may identify the object in the other frame based on analyzing the other frame. In some implementations, a user of the annotation system105may manually select the object in the other frame of the video and apply a wire model.

The annotation system105may automatically insert or overlay the adjusted wire model on the object in the other frame of the video. The annotation system105may overlay the adjusted wire model on a location in the other frame of the video where the object appears. As shown inFIG.1E, the object (e.g., the vehicle) may be located to the right in the other frame of the video, and the annotation system105may align the adjusted wire model on the location of the object in the other frame of the video. In some implementations, an orientation and/or a size of the adjusted wire model may not be exactly the same as an orientation and/or a size of the object in the other frame. In such implementations, the annotation system105may adjust one or more of an orientation, a location, and/or a size of the adjusted wire model on the object in the other frame based on the corresponding camera information, as described above in connection withFIG.1D.

As shown inFIG.1F, and by reference number155, the annotation system105may interpolate the adjusted wire model for the object for intermediate frames of the video between the frame and the other frame. For example, the annotation system105may identify the intermediate frames of the video that are provided between the frame and the other frame. The annotation system105may determine whether the object appears in the intermediate frames of the video, and may determine whether the adjusted wire model aligns with the object in the intermediate frames (e.g., when the object appears in the intermediate frames). In some implementations, an orientation and/or a size of the adjusted wire model may not be exactly the same as an orientation and/or a size of the object in an intermediate frame. In such implementations, the annotation system105may adjust one or more of an orientation, a location, and/or a size of the adjusted wire model on the object in the intermediate frame based on the corresponding camera information, as described above in connection withFIG.1D. Interpolation of the adjusted wire model for the object in the intermediate frames may thereby quickly generate three-dimensional annotations for the video without the need for a user to manually place wire models and indicate that the objects in the intermediate frames are the same object.

Interpolation of the adjusted wire model for the object in the intermediate frames is accurate because the positions and orientations of the object are calculated for the intermediate frames using three-dimensional information. For example, the annotation system105may linearly interpolate a track of the object (e.g., in the intermediate frames) based on three-dimensional data (e.g., x, y, and z positions) and orientation data (e.g., yaw, pitch, roll, and/or the like).FIG.1Fillustrates a benefit of the three-dimensional interpolation process. The lower and larger bounding boxes inFIG.1Fare a result of three-dimensional based interpolation for the intermediate frames of the vehicle moving in the video. The upper and smaller bounding boxes inFIG.1Fare a result of two-dimensional based interpolation for the intermediate frames of the same vehicle. As can be seen, the three-dimensional based interpolation is far more accurate than what would be generated using the two-dimensional based interpolation, as the three-dimensional based interpolation takes into account the rules of projective geometry and camera lens distortion that are applicable to working with a two-dimensional video source. When the vehicle is traveling within a velocity range (e.g., within a one kilometers per hour (kph) range, a two kph range, a three kph range, and/or the like), the annotated frame and the annotated other frame may be enough information for the annotation system105to determine annotations for the intermediate frames.

As further shown inFIG.1F, and by reference number160, the annotation system105may generate three-dimensional annotations for the video based on the adjusted wire models for the frame, the intermediate frames, and the other frame. For example, the annotation system105may utilize the type, the locations, the sizes, the orientations, and/or the like of the adjusted wire frame models, overlayed in the frame, the intermediate frames, and the other frame, as the three-dimensional annotations for the video.

In some implementations, the annotation system105may quickly generate reliable and accurate three-dimensional annotations for the video. The annotation system105may utilize the camera information to transform the two-dimensional video data into a three-dimensional object representation, where an orientation of an object in the video may be determined with a single angle (e.g., instead of three angles, as in current systems). Thus, the three-dimensional annotations are more accurate than two-dimensional based annotations because the annotation system105utilizes fewer parameters.

In some implementations, a user of the annotation system105may review the generated annotations and decide that the generated annotations are not accurate enough. For example, the interpolated annotations for an object may not be accurate due to a change in object velocity or other movement discontinuity that occurs between the first frame and the second frame. In such cases, the user may choose an intermediate frame (a “third” frame) between the first and second frames and apply a wire model annotation to the object in that frame. The annotation system105may then perform an interpolation of the images between the first frame and the third frame, and an interpolation of the images between the third frame and the second frame, and display the results similar to those shown inFIG.1F.

In some implementations, the annotation system105may provide segmentation-like annotations by enabling three-dimensional to two-dimensional transformations that transform the three-dimensional wire models back to a two-dimensional projected space. A concave hull of a two-dimensional projected wire model of an object may include a segmentation polygon that provides a more precise approximation of object pixels to a machine learning model.

In some implementations, the annotation system105may perform occlusion calculations with three-dimensional position data. The annotation system105may utilize the three-dimensional position data to calculate distances of objects from a camera, and may utilize the distances to determine whether an object covers (e.g., occludes) another object, and an occlusion level. This occlusion level may be useful for training a machine learning model. For example, the annotation system105may exclude objects with low visibility (e.g., a high occlusion level) from training data for the machine learning model. The three-dimensional wire models may improve accuracies of the occlusion levels since rectangles or cuboids may include a large quantity of pixels that actually do not belong to an object.

FIG.1Gprovides an example image of a pickup truck and trailer that partially occludes a portion of a sedan. As shown, the annotation system105may utilize three-dimensional position data of the pickup truck, the trailer, and the sedan to calculate distances of the pickup truck, the trailer, and the sedan from a camera, and may utilize the distances to determine that there is occlusion between the pickup truck and trailer and the sedan, and an occlusion level, which allows more accurate placing of wire models reflective of the actual objects than would be the case if the wire models used two-dimensional images.

As shown inFIG.1H, and by reference number165, the annotation system105may train, test, and validate a machine learning model based on the 3D annotations. For example, the annotation system105may divide the 3D annotations into a first portion of 3D annotations, a second portion of 3D annotations, and a third portion of 3D annotations. The first portion, the second portion, and the third portion may include a same quantity of the 3D annotations, different quantities of the 3D annotations, and/or the like. In some implementations, more of the 3D annotations may be allotted to the first portion of 3D annotations since the first portion may be utilized to generate a training data set for the machine learning model.

The annotation system105may generate the training dataset for the machine learning model based on the first portion of 3D annotations. The annotation system105may generate a validation dataset for the machine learning model based on the second portion of 3D annotations. The annotation system105may generate a test dataset for the machine learning model based on the third portion of 3D annotations. In other implementations, the annotation system105may utilize different portions of the 3D annotations to generate the training dataset, the validation dataset, and/or the test dataset for the machine learning model.

The annotation system105may train the machine learning model with the training dataset to generate the trained machine learning model. The machine learning model may be trained to detect one or more of sizes, speeds, or geographical locations of objects in videos processed by the machine learning model. The machine learning model may be utilized in a service that includes a computer vision service. Examples of such services include a mobility service (e.g., planning and paying for a transportation service), a vision zero service (e.g., a road safety service), or a social distancing service (e.g., crowd control or foot traffic planning), and/or the like. In some implementations, rather than training the machine learning model, the annotation system105may obtain the machine learning model from another system or device that trained the machine learning model. In this case, the annotation system105may provide the other system or device with the training dataset, the validation dataset, and/or the test dataset for use in training the machine learning model, and may provide the other system or device with updated training, validation, and/or test datasets to retrain the machine learning model in order to update the machine learning model.

In some implementations, the machine learning model may include a neural network model, such as a convolutional neural network (CNN) model. A neural network model may include a collection of connected units or nodes. Each connection (e.g., edge) can transmit a signal to other nodes. A node receives and processes a signal and may signal nodes connected to the node. A signal at a connection is a real number, and an output of each node is computed by a non-linear function of the sum of inputs. The nodes and the connections may include weights that adjust as learning proceeds. The weights increase or decrease a strength of a signal at a connection. Typically, the nodes may be aggregated into layers. Different layers may perform different transformations on inputs. Signals may travel from a first layer (an input layer), to a last layer (an output layer), possibly after traversing the layers multiple times.

In some implementations, the annotation system105may train the machine learning model with the training dataset to generate the trained machine learning model, and may process the validation dataset, with the trained machine learning model, to validate that the trained machine learning model is operating correctly. If the trained machine learning model is operating correctly, the annotation system105may process the trained machine learning model, with the test dataset, to further ensure that the trained machine learning model is operating correctly. If the trained machine learning model is operating incorrectly, the annotation system105may modify the trained machine learning model and may revalidate and/or retest the modified machine learning model based on the validation dataset and/or the test dataset.

In this way, the annotation system105generates three-dimensional annotations for training a machine learning model. For example, the annotation system105may create high-quality annotation data for the machine learning models in a timely, accurate and inexpensive manner. The high-quality annotation data may be utilized to train machine learning models (e.g., neural network models) to better identify and estimate three-dimensional properties of detected objects (e.g., sizes, speeds, real world locations, and/or the like) in a computer vision system, such as may be used as part of mobility, vision zero, crowd management, and similar use cases. Thus, the annotation system105may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by generating poor-quality annotation data, generating insufficient machine learning models based on the poor-quality annotation data, utilizing incorrect predictions generated by the insufficient machine learning models, and/or the like.

As indicated above,FIGS.1A-1Hare provided as an example. Other examples may differ from what is described with regard toFIGS.1A-1H. The number and arrangement of devices shown inFIGS.1A-1Hare provided as an example. In practice, there may be additional devices, fewer devices, different devices, or differently arranged devices than those shown inFIGS.1A-1H. Furthermore, two or more devices shown inFIGS.1A-1Hmay be implemented within a single device, or a single device shown inFIGS.1A-1Hmay be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) shown inFIGS.1A-1Hmay perform one or more functions described as being performed by another set of devices shown inFIGS.1A-1H.

FIG.2is a diagram of an example environment200in which systems and/or methods described herein may be implemented. As shown inFIG.2, environment200may include the annotation system105, which may include one or more elements of and/or may execute within a cloud computing system202. The cloud computing system202may include one or more elements203-213, as described in more detail below. As further shown inFIG.2, environment200may include the data structure110and/or a network220. Devices and/or elements of environment200may interconnect via wired connections and/or wireless connections.

The data structure110includes one or more devices capable of receiving, generating, storing, processing, and/or providing information, as described elsewhere herein. The data structure110may include a communication device and/or a computing device. For example, the data structure110may include a database, a server, a database server, an application server, a client server, a web server, a host server, a proxy server, a virtual server (e.g., executing on computing hardware), a server in a cloud computing system, a device that includes computing hardware used in a cloud computing environment, or a similar type of device. The data structure110may communicate with one or more other devices of the environment200, as described elsewhere herein.

The cloud computing system202includes computing hardware203, a resource management component204, a host operating system (OS)205, and/or one or more virtual computing systems206. The cloud computing system202may execute on, for example, an Amazon Web Services platform, a Microsoft Azure platform, or a Snowflake platform. The resource management component204may perform virtualization (e.g., abstraction) of the computing hardware203to create the one or more virtual computing systems206. Using virtualization, the resource management component204enables a single computing device (e.g., a computer or a server) to operate like multiple computing devices, such as by creating multiple isolated virtual computing systems206from the computing hardware203of the single computing device. In this way, the computing hardware203can operate more efficiently, with lower power consumption, higher reliability, higher availability, higher utilization, greater flexibility, and lower cost than using separate computing devices.

The computing hardware203includes hardware and corresponding resources from one or more computing devices. For example, the computing hardware203may include hardware from a single computing device (e.g., a single server) or from multiple computing devices (e.g., multiple servers), such as multiple computing devices in one or more data centers. As shown, the computing hardware203may include one or more processors207, one or more memories208, one or more storage components209, and/or one or more networking components210. Examples of a processor, a memory, a storage component, and a networking component (e.g., a communication component) are described elsewhere herein.

The resource management component204includes a virtualization application (e.g., executing on hardware, such as the computing hardware203) capable of virtualizing computing hardware203to start, stop, and/or manage one or more virtual computing systems206. For example, the resource management component204may include a hypervisor (e.g., a bare-metal or Type 1 hypervisor, a hosted or Type 2 hypervisor, or another type of hypervisor) or a virtual machine monitor, such as when the virtual computing systems206are virtual machines211. Additionally, or alternatively, the resource management component204may include a container manager, such as when the virtual computing systems206are containers212. In some implementations, the resource management component204executes within and/or in coordination with a host operating system205.

A virtual computing system206includes a virtual environment that enables cloud-based execution of operations and/or processes described herein using the computing hardware203. As shown, the virtual computing system206may include a virtual machine211, a container212, or a hybrid environment213that includes a virtual machine and a container, among other examples. The virtual computing system206may execute one or more applications using a file system that includes binary files, software libraries, and/or other resources required to execute applications on a guest operating system (e.g., within the virtual computing system206) or the host operating system205.

Although the annotation system105may include one or more elements203-213of the cloud computing system202, may execute within the cloud computing system202, and/or may be hosted within the cloud computing system202, in some implementations, the annotation system105may not be cloud-based (e.g., may be implemented outside of a cloud computing system) or may be partially cloud-based. For example, the annotation system105may include one or more devices that are not part of the cloud computing system202, such as the device300ofFIG.3, which may include a standalone server or another type of computing device. The annotation system105may perform one or more operations and/or processes described in more detail elsewhere herein.

The network220includes one or more wired and/or wireless networks. For example, the network220may include a cellular network, a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a private network, the Internet, and/or a combination of these or other types of networks. The network220enables communication among the devices of the environment200.

The number and arrangement of devices and networks shown inFIG.2are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown inFIG.2. Furthermore, two or more devices shown inFIG.2may be implemented within a single device, or a single device shown inFIG.2may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of the environment200may perform one or more functions described as being performed by another set of devices of the environment200.

FIG.3is a diagram of example components of a device300, which may correspond to the annotation system105and/or the annotation data structure110. In some implementations, the annotation system105and/or the annotation data structure110may include one or more devices300and/or one or more components of the device300. As shown inFIG.3, the device300may include a bus310, a processor320, a memory330, an input component340, an output component350, and a communication component360.

The bus310includes one or more components that enable wired and/or wireless communication among the components of the device300. The bus310may couple together two or more components ofFIG.3, such as via operative coupling, communicative coupling, electronic coupling, and/or electric coupling. The processor320includes a central processing unit, a graphics processing unit, a microprocessor, a controller, a microcontroller, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, and/or another type of processing component. The processor320is implemented in hardware, firmware, or a combination of hardware and software. In some implementations, the processor320includes one or more processors capable of being programmed to perform one or more operations or processes described elsewhere herein.

The memory330includes volatile and/or nonvolatile memory. For example, the memory330may include random access memory (RAM), read only memory (ROM), a hard disk drive, and/or another type of memory (e.g., a flash memory, a magnetic memory, and/or an optical memory). The memory330may include internal memory (e.g., RAM, ROM, or a hard disk drive) and/or removable memory (e.g., removable via a universal serial bus connection). The memory330may be a non-transitory computer-readable medium. Memory330stores information, instructions, and/or software (e.g., one or more software applications) related to the operation of the device300. In some implementations, the memory330includes one or more memories that are coupled to one or more processors (e.g., the processor320), such as via the bus310.

The input component340enables the device300to receive input, such as user input and/or sensed input. For example, the input component340may include a touch screen, a keyboard, a keypad, a mouse, a button, a microphone, a switch, a sensor, a global positioning system sensor, an accelerometer, a gyroscope, and/or an actuator. The output component350enables the device300to provide output, such as via a display, a speaker, and/or a light-emitting diode. The communication component360enables the device300to communicate with other devices via a wired connection and/or a wireless connection. For example, the communication component360may include a receiver, a transmitter, a transceiver, a modem, a network interface card, and/or an antenna.

The device300may perform one or more operations or processes described herein. For example, a non-transitory computer-readable medium (e.g., the memory330) may store a set of instructions (e.g., one or more instructions or code) for execution by the processor320. The processor320may execute the set of instructions to perform one or more operations or processes described herein. In some implementations, execution of the set of instructions, by one or more processors320, causes the one or more processors320and/or the device300to perform one or more operations or processes described herein. In some implementations, hardwired circuitry may be used instead of or in combination with the instructions to perform one or more operations or processes described herein. Additionally, or alternatively, the processor320may be configured to perform one or more operations or processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

The number and arrangement of components shown inFIG.3are provided as an example. The device300may include additional components, fewer components, different components, or differently arranged components than those shown inFIG.3. Additionally, or alternatively, a set of components (e.g., one or more components) of the device300may perform one or more functions described as being performed by another set of components of the device300.

FIG.4is a flowchart of an example process400for generating three-dimensional annotations for training a machine learning model. In some implementations, one or more process blocks ofFIG.4may be performed by a device (e.g., the annotation system105). In some implementations, one or more process blocks ofFIG.4may be performed by another device or a group of devices separate from or including the device. Additionally, or alternatively, one or more process blocks ofFIG.4may be performed by one or more components of the device300, such as the processor320, the memory330, the input component340, the output component350, and/or the communication component360.

As shown inFIG.4, process400may include receiving a video and corresponding camera information associated with a camera that captured the video (block410). For example, the device may receive a video and corresponding camera information associated with a camera that captured the video, as described above. In some implementations, the corresponding camera information includes information identifying one or more of lens distortion parameters associated with the camera, a geographical location of the camera, or a position of the camera. In some implementations, the video is a two-dimensional representation of a three-dimensional scene. In some implementations, the corresponding camera information includes information identifying one or more of stereo imaging associated with the camera, light detection and ranging location associated with the camera, or parameters associated with the camera.

As further shown inFIG.4, process400may include selecting an object in the video and a wire model for the object (block420). For example, the device may select an object in the video and a wire model for the object, as described above.

As further shown inFIG.4, process400may include adjusting one or more of an orientation, a location, or a size of the wire model to align the wire model on the object in a frame of the video (block430). For example, the device may adjust one or more of an orientation, a location, or a size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model, as described above.

As further shown inFIG.4, process400may include identifying the object in another frame of the video (block440). For example, the device may identify the object in another frame of the video, as described above.

As further shown inFIG.4, process400may include aligning the adjusted wire model on the object in the other frame (block450). For example, the device may align the adjusted wire model on the object in the other frame, as described above.

As further shown inFIG.4, process400may include interpolating the adjusted wire model for the object for intermediate frames of the video (block460). For example, the device may interpolate the adjusted wire model for the object for intermediate frames of the video between the frame and the other frame, as described above. In some implementations, interpolating the adjusted wire model for the object for the intermediate frames includes linearly interpolating the adjusted wire model for the object for the intermediate frames based on three-dimensional position data of the object and three-dimensional orientation of the object.

As further shown inFIG.4, process400may include generating 3D annotations for the video based on the adjusted wire models for the frame, the intermediate frame, and the other frame (block470). For example, the device may generate 3D annotations for the video based on the adjusted wire models for the frame, the intermediate frames, and the other frame, as described above. In some implementations, the wire model is a three-dimensional segmentation polygon and the three-dimensional annotations are three-dimensional segmentation-based annotations.

As further shown inFIG.4, process400may include training a machine learning model based on the 3D annotations (block480). For example, the device may train a machine learning model based on the 3D annotations, as described above. In some implementations, the machine learning model is a neural network model.

In some implementations, process400includes receiving camera information identifying calibration and registration information for a plurality of cameras used to generate a plurality of videos; receiving the plurality of videos generated by the plurality of cameras; and mapping, in an annotation data structure, the camera information with corresponding videos based on camera identifiers associated with the plurality of cameras, where the video and the corresponding camera information are received from the annotation data structure.

In some implementations, process400includes testing the machine learning model based on the three-dimensional annotations. In some implementations, process400includes validating the machine learning model based on the three-dimensional annotations. In some implementations, process400includes calculating an occlusion level of the object with another object in the video based on the three-dimensional annotations, and training the machine learning model based on the occlusion level.

In some implementations, process400includes causing the machine learning model to be utilized to detect one or more of sizes, speeds, or geographical locations of objects in videos processed by the machine learning model. In some implementations, process400includes causing the machine learning model to be utilized in a computer vision service, such as used in one or more of a mobility service, a vision zero service, or a crowd management service.

AlthoughFIG.4shows example blocks of process400, in some implementations, process400may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted inFIG.4. Additionally, or alternatively, two or more of the blocks of process400may be performed in parallel.

As used herein, the term “component” is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. It will be apparent that systems and/or methods described herein may be implemented in different forms of hardware, firmware, and/or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods are described herein without reference to specific software code—it being understood that software and hardware can be used to implement the systems and/or methods based on the description herein.

As used herein, satisfying a threshold may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, not equal to the threshold, or the like.

To the extent the aforementioned implementations collect, store, or employ personal information of individuals, it should be understood that such information shall be used in accordance with all applicable laws concerning protection of personal information. Additionally, the collection, storage, and use of such information can be subject to consent of the individual to such activity, for example, through well known “opt-in” or “opt-out” processes as can be appropriate for the situation and type of information. Storage and use of personal information can be in an appropriately secure manner reflective of the type of information, for example, through various encryption and anonymization techniques for particularly sensitive information.

Even though particular combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of various implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various implementations includes each dependent claim in combination with every other claim in the claim set. As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more.” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, or a combination of related and unrelated items), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and/or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of”).

In the preceding specification, various example embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.