Methods and systems for detecting and analyzing a region of interest from multiple points of view

Disclosed are devices, systems, methods, techniques, and computer program products for estimating a Region Of Interest (ROI) corresponding to a plurality of content streams. The method may include receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices. The plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices. Further, each of the plurality of mobile devices may include at least one recorder configured to capture a corresponding content steam. Further, a sensor data associated with a mobile device may include a location data and an orientation data of the mobile device during capturing of a content stream. The method may further include, analyzing, by the system, the plurality of sensor data and determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI.

RELATED APPLICATION

Related U. S. patent application Ser. No. 15/668,540 filed on even date herewith entitled “Method And System For Aggregating Content Streams Based On Sensor Data”, assigned to the assignee of the present application, is hereby incorporated by reference. It is intended that the referenced application may be applicable to the concepts and embodiments disclosed herein, even if such concepts and embodiments are disclosed in the referenced application with different limitations and configurations and described using different examples and terminology.

FIELD OF DISCLOSURE

The present disclosure generally relates to digital data processing. More specifically, the disclosure relates to a method and system for estimating a Region Of Interest (ROI) corresponding to a plurality of content streams.

BACKGROUND

As users increasingly turn to the internet for all their information needs, there is a constant pressure on information providers to expand their databases with newer data. One example of such data relates to events. Accordingly, there are several information providers who maintain event databases that inform users about a time and a place where an event (e.g. a sporting event, a music concert, a conference, etc.) is scheduled to occur. Additionally, such event databases may also include information about currently ongoing events and/or completed events. Further, in case of events that are completed, a recording (audio, video, multimedia etc.) of the event are typically available to users.

However, conventionally, such event databases are constructed and maintained largely based on manual efforts. For example, content reviewers scour various communication channels (e.g. newspapers, magazines, direct communication from event organizers, radio, television, advertisements etc.) in order to identify events and accordingly update an event database.

Firstly, the manual process of discovering events and updating event databases is cumbersome and time consuming. Typically, individuals delegated with the task of searching and identifying events and associated event information (type of event, time, place, etc.) are burdened. Further, while some event databases allow general users to provide such event information (i.e. crowdsourcing of event information), reviewing such information for correctness is still a difficult and time consuming process.

Secondly, due to the limited number of communication channels that are searched by content reviewers, a large number of interesting events that are not advertised on such communication channels are missed. For example, there may be several local events which may not garner an advertising campaign. However, users situated in the vicinity of such events may nonetheless be highly interested in these local events.

Thirdly, some events may occur on an ad-hoc basis and/or in an unorganized manner, which thus makes it difficult to collect and provision corresponding event information to users in a timely fashion.

Further, while currently there are techniques of aggregating content streams based on location, topic, etc., such aggregation of content streams may not necessarily correspond to an event. For example, at a place with high tourist foot-fall, a large number of users may be capturing and streaming video of different regions and/or subjects which may not correspond to a singular event.

Fourthly, while an event may be spread across a large spatial region, there may be one or more smaller regions of interest at different times during the event's progression. For example, at an event involving a large stage, interesting action may take place on different regions of the stage at different times. However, existing methods do not provide an automatic and reliable way of determining such regions of interest for a user's benefit.

Lastly, while users are currently offered the ability to consume a video recording of an event from different view points, there is no intuitive user interface that enables users to effectively navigate across multiple view points to experience the event from different perspectives.

Accordingly, there is a need for methods and systems for automatically discovering events and identifying regions of interest in such events in a manner that overcomes one or more of the above mentioned drawbacks.

DETAILED DESCRIPTION

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of content streams such as live audio/video streams, embodiments of the present disclosure are not limited to use only in this context. For instance, the methods and systems disclosed herein may be used to aggregate any digital content based on corresponding metadata (for example, but not limited to, sensor data) associated with the digital content.

I. PLATFORM OVERVIEW

Consistent with embodiments of the present disclosure, methods, computer-readable media, and systems for Region of Interest (ROI) estimation and representation (also referred to herein as “platform” or ROI estimation platform) may be provided. Estimation and/or representation of a ROI may at least be based on sensor data including location data and orientation data corresponding to recording devices. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope. The platform may be used by individuals or companies to estimate ROI based on sensor data, such as, location data and orientation data corresponding to recording devices. Thus, according to exemplary aspects, the present disclosure provides methods, systems, and/or techniques for discovering an event and a region of interest associated with the event.

In another exemplary aspect, the present disclosure describes techniques of estimating a ROI using data provided by location and orientation sensors built into a smart mobile device to determine a live stream point of view (POV). The POVs from multiple smart mobile devices at the same relative location or venue can be analyzed to determine the Region of Interest (ROI) for an event at that location. The ROI can then be visualized graphically on a viewer's smart mobile device or television to aid in understanding an event's location with no prior knowledge of the size and shape of the venue.

In another exemplary aspect, the present disclosure provides a method to collect and analyze a plurality of data streams to compute an approximate size and shape of the ROI at a venue with no previous knowledge of the venue. In some aspects, two or more people may be present at the venue and that two or more of the people are pointing the camera on their smart mobile devices at something within the ROI. In further aspects, the accuracy of the computed area and/or volume may be relative to the number of people pointing their devices at the ROI. The method can be used to calculate two-dimensional or three-dimensional volumes.

In various aspects, each person's mobile smart device, such as a modern smart phone, tablet, or wearable includes sensors to measure global position, compass heading, and acceleration of movement in three dimensions. The data from each of these smart mobile devices, in addition to the make and model of the device, can be streamed over a collection of wireless and wired networks to a computer server and data storage. The server can then processes the data from each device. The global positioning system coordinates can be converted to an origin point in a mapping coordinate system. The compass heading angle can be converted to a vector, or specifically a direction from the origin point. The combination of the origin point and the vector can mathematically represent the position and direction that the person is pointing their smart mobile device, relative to other people's devices at the venue.

In further aspects, an array of previously collected data about the make and model of smart mobile devices can be used to provide lens characteristics for the specific smart mobile device. The lens characteristics can be used to determine the ever-expanding field of view (FOV) for the specific smart mobile device's camera. In yet further aspects, the FOV can be represented geometrically as an expanding square frustum.

In further aspects, the origin points of all smart mobile devices collecting data at the venue can be placed in the same coordinate system so that their position is accurate relative to the position of the actual people pointing their devices in the real venue. In some aspects, a two-dimensional line segment representing the length or magnitude of the directional vectors for each device can be extended from their origin point to a pre-estimated maximum length, less than infinity, to cover the size of a very large venue, such as a stadium or park.

Intersections of these vectors can represent shared areas being pointed at by people. The specific camera frustum can be overlaid onto each smart mobile device's origin point with the directional vector as the center expanding direction of the frustum. In still further aspects, the outer edges of the intersections of the expanded view areas can be connected to form a polygonal representation of the shared ROI.

In some aspects, such as where devices appear to be pointing from one side of a 180° line, such as with a smaller concert venue or amphitheater, the closest interior origin point can be used to estimate the beginning depth of the ROI. In further aspects, the type of event, such as a concert, could be used to estimate the maximum depth of the ROI.

In other aspects, such as where devices appear to be pointing from all sides, such as a stadium or large sporting event, a center point or centroid can be computed from all origin points. In further aspects, the distance from the centroid can be computed for every origin point. In still further aspects, a number of directions can be sampled, such from greater than one, for example, four or sixteen. In yet further aspects, the closest origin point from the centroid in every sampled direction can be connected to estimate the containing shape of the ROI.

In further aspects, the region of interest is at least based on analysis of content streams captured by multiple recording devices (e.g. smartphone cameras, portable cameras, wearable cameras, etc.) from different view points. In still further aspects, data related to positions and orientations of multiple recording devices can be analyzed to determine a Region Of Interest (ROI) towards which the multiple recording devices are pointed. In yet further aspects, determination of the ROI can further comprise discovery of an event captured by users of the multiple recording devices. In even further aspects, optical characteristics (e.g. field of view) of the multiple recording devices can be utilized to determine attributes of the ROI such as, size, shape, volume, etc. As a result, a graphical representation of the ROI may be provided to users depicting a spatial form of the ROI with relative positions of the multiple recording devices. Such a graphical representation may allow users to intuitively visualize the ROI and other available viewpoints of the ROI. Thus, in conjunction with access to content streams captured by the multiple recording devices, users may be provided with an intuitive and powerful user interface for navigating across multiple view points and consuming the content streams that include the ROI.

In further aspects, the multiple recording devices may be configured to capture and transmit sensor data representing position and orientation of respective recording devices. Accordingly, each recording device may include a position sensor (e.g. GPS sensor) and an orientation sensor (e.g. magnetic compass). In still further aspects, the sensor data may include a time indicator corresponding to sensing of the position and orientation of respective recording devices. Such sensor data may, in some aspects, be transmitted along with content (e.g. video, audio etc.) captured by respective recording devices.

According to various aspects, the sensor data may be processed by one or more computing devices or recording devices in connection with one or more stages of the disclosed methods and systems. For example, the sensor data may be embedded as metadata in a content stream captured and transmitted by a recording device, such as a smartphone. In further aspects, the smartphone may be provided with an app configured for capturing and transmitting the sensor data to a computing device, such as central computing device, for example, a server. In still further aspects, the app may be configured for embedding the sensor data during capturing of content. In yet further aspects, the recording devices may be configured to only transmit sensor data along with corresponding time indicators to indicate a respective time (e.g. date and time of day) when the sensor data was captured.

In further aspects, transmitted sensor data in multiple content streams may be received by a central computing device, such as a server. In some aspects, the server may process or sort the multiple content streams according to one or more attributes such as, location, time, orientation, etc. By way of non-limiting example, initially, the server may process or sort the multiple content streams based on temporal and spatial proximity. To this end, content streams which were captured within a short time window (e.g. few hours, minutes, etc.) and at a common place (e.g. a sports stadium, amphitheater, concert venue, etc.) may be aggregated. In other aspects, transmitted data from can also be received and processed by another smartphone (e.g., another user of the platform). For example, a user of the disclosed platform and systems may receive and utilize data transmitted from another user's smartphone (e.g. a different user viewing a common event) to determine and produce an ROI. In further aspects, the shared data can be unprocessed sensor data, or data processed by a user to determine a ROI, or combinations thereof.

Further, sensor data (i.e. position and orientation) in the multiple content streams may be analyzed in order to determine one or more ROIs. Accordingly, each recording device may be represented as an origin point in a coordinate space constructed by the server. Accordingly, the positions (i.e. geographical locations) of the multiple recording devices may be mapped in the coordinate space while preserving a spatial relationship among the positions. For example, a first recording device, a second recording device and a third recording device may be away from each other by 100 m. In other words, the three recording devices may be located at vertices of an equilateral triangle. Accordingly, three origin points may be created in the coordinate space with the same spatial relationship. In other words, the three origin points may be equidistant from each other.

Further, each origin point may be associated with a vector (2D or 3D vector) representing an orientation of a respective recording device. In further aspects, a first recording device may be represented by a first origin point and a first vector in the coordinate space, wherein spatial coordinates of the first recording device is based on position of the first recording device, while a direction of the first vector is based on orientation of the first recording device. In still further aspects, each of the vectors may be extended indefinitely or until they reach a surface of a bounding box of the coordinate space. In yet further aspects, the bounding box may be of arbitrary form and significantly larger than the biggest ROI determinable.

Subsequent to extending the vectors corresponding to the multiple origin points, intersections among the vectors may be determined. For example, given a first origin point and a first vector corresponding to the first origin point, a first line in the coordinate space may be computed. Similarly, a second line may be computed based on a second origin point and a second vector. Further, using coordinate geometry, it may be computationally determined whether the first line intersects the second line within the coordinate space. Further, an intersection point corresponding to the first line and the second line may also be computationally determined. Accordingly, multiple such intersection points may be determined corresponding to a plurality of pairs of lines. Further, in some aspects, lines may overlap each other resulting in a plurality of intersection points for a given pair of lines. Furthermore, the number of such intersection points may be based on a sampling distance in the coordinate space.

In further aspects, a spatial distribution of the intersection points may be computationally analyzed. Accordingly, one or more spatial characteristics associated with the multiple intersection points may be computed. For example, a density of intersection points at various points within the coordinate space may be computed. Accordingly, spatial regions with density exceeding a threshold value may be identified as being coincident with the ROI. In other words, a spatial concentration of intersection points can, in various aspects, indicate presence of the ROI. Additionally, a spatial distribution of the intersection points may indicate attributes of the ROI, such as, shape, size, area, volume, etc. Accordingly, in some aspects, by computing a boundary associated with the spatial concentration of intersection points, a shape of the ROI may be determined. Further, attributes of the ROI such as area and volume may then be computationally determined based on the boundary.

Further, by considering one or more optical characteristics of the multiple recording devices, estimation of the ROI may be performed with higher accuracy. For example, field of view (FOV) corresponding to the multiple recording devices may be taken into account in determining the ROI. Accordingly, the multiple recording devices may be configured to transmit respective FOV values to the server. Alternatively, the multiple recording devices may be configured to transmit device information (i.e. model number, product code etc.) based on which the server may determine corresponding FOV values by performing a database search.

Subsequently, for each origin point and corresponding vector, a viewing frustum (2D or 3D) may be constructed in the coordinate space at least based on a FOV value associated with the origin point. Further, an apex of a viewing frustum associated with a recording device may be coincident with an origin point associated with the recording device. Furthermore, a view direction of the viewing frustum may be aligned with the vector associated with the origin point. Subsequently, a plurality of intersections of multiple viewing frustums may be determined. Such determination may be performed computationally using coordinate geometry. Further, by computationally analyzing characteristics of the plurality of intersections of viewing frustums, the ROI may be estimated.

Subsequent to determining the ROI, a visualization of the ROI may be generated by the system. The visualization of the ROI may be a geometrical figure (2D or 3D) depicting the shape and orientation of the ROI. For example, a performance stage which may be of circular form may be represented as a circle in the visualization. Additionally, the visualization may include visual representation of the multiple recording devices according to respective positions in relation to the ROI. In other words, the visualization of the ROI may also depict where the multiple recording devices are spatially situated in relation to the ROI. As a result, the visualization enables users to view a spatial form of the ROI and also the multiple viewpoints that are available. Accordingly, a user may select a representation of a visualization in the particular recording device in order to express an interest in a corresponding view point. In some aspects, the visualization may be configured to receive user inputs associated with different regions of the visualization. For example, the visualization displayed on a touch-screen may be configured to receive touch inputs on points corresponding to the multiple recording devices. Consequently, the content stream captured by the particular recording device may be retrieved and displayed to the user. As a result, the visualization may function as a spatial user interface that may enable users to intuitively navigate across multiple viewpoints and consume corresponding multiple content streams.

According to further exemplary aspects, the present disclosure provides at least two methods related to live streaming events. A first method includes collecting, streaming, and collectively analyzing sensor data from the smart mobile devices of live streamers at the same event to determine and visualize the relative position and orientation of the streamers. A second method includes using that data and analysis to estimate the size and shape of the Region of Interest being covered by the collection of live streamers.

As described herein, determining an estimated ROI has several benefits. In further aspects, a use related to live streaming events enables a user interface element to visualize multiple available viewpoints around the most likely Region of Interest. In still further aspects, this aids the user in understanding the likely size and shape of the volume that they are navigating when exploring multiple points of view around an event.

I. Embodiments of the present disclosure provide a software and hardware platform comprised of a distributed set of modules, including, but not limited to:A. Sensor Data Generation Module; andB. ROI Estimation Module.

In some embodiments, the present disclosure may provide an additional set of modules for further facilitating the software and hardware platform. The additional set of modules may comprise, but not be limited to:C. ROI Display Module; andD. Content Transmission/Streaming Module.

FIGS. 1A and 1Billustrate non-limiting examples of operating environments for the aforementioned modules. Although modules are disclosed with specific functionality, it should be understood that functionality may be shared between modules, with some functions split between modules, while other functions duplicated by the modules. Furthermore, the name of the module should not be construed as limiting upon the functionality of the module. Moreover, each stage in the claim language can be considered independently without the context of the other stages. Each stage may contain language defined in other portions of this specifications. Each stage disclosed for one module may be mixed with the operational stages of another module. Each stage can be claimed on its own and/or interchangeably with other stages of other modules. The following claims will detail the operation of each module, and inter-operation between modules.

II. Embodiments of the present disclosure provide a software and hardware platform comprised of a distributed set of computing elements, including, but not limited to:A Computing Device(FIG. 15—Computing Device1500)Wherein the platform is operative to control a computing device in furtherance of the operation of the content generation module,The computing device comprising, but not limited to at least one of the following:A processing unit,A memory storage,Wherein the computing device may be embodied as a mobile computing device,wherein the mobile computing device comprises, but is not limited to,A tablet,A smartphone,A drone,A wearable (such as watch or smart glasses), and,A remotely operable recording device;Wherein the computing device may comprise sensing devices,wherein the sensing device may comprise, but is not limited to,A camera, andA microphone;A location sensor;An orientation sensor;A motion sensor;A speed sensor; andWherein the computing device may be in communication with the sensing devices, wherein the sensing devices provide telemetry data associated with the computing device.Sub-Modules Associated with the Computing Device(FIG. 15—Program Modules1506)Wherein the platform is operative to control at least one of the following sub-modules of a computing device:A user interface module,A sensor data generation module,i. A location module,ii. An orientation moduleA content capturing module,A timing module, andA communications module.1. The User Interface Modulea. Enables user-control of the Computing Deviceb. Enables user-control of the Sub-Modules of the Computing Devicei. The user interface moduleii. The sensor data generation moduleiii. The content capturing moduleiv. The timing modulev. The location modulevi. The communications modulec. Enables user-control of the Platform Modules:i. The sensor data generation moduleii. The content capturing moduleiii. The content transmission moduleiv. The ROI estimation modulev. The ROI display module2. The Sensor Data Generation Modulea. Enables operative control of sensing devices comprised in the recording devicesi. Sensing Devices1. Location sensor2. Orientation sensor3. Motion sensor4. Speed sensorb. Enables Digital Signal Processing on sensor data:i. Enables filtering, but not limited to, location, time, orientation, speed, direction of motionii. Enables image processing techniques such as, but not limited to, motion stabilization, low pass filtering, noise removal3. The Content Capturing Modulea. Enables operative control of content recordation hardwarei. Sensing Device1. Optical Sensors2. Audio Sensors3. Telemetry Sensorsb. Enables capturing based on data:i. Recordation of content received from the communications moduleii. Recordation of content displayed on the computing device (e.g., screen capture)c. Enables Digital Signal Processing on captured content:i. Enables Content Stitching based on, but not limited to, spatial parameters and temporal parametersii. Enables image processing techniques such as, but not limited to, image stabilization.4. The Timing Modulea. Operative control of a clock associated with the computing deviceb. In operative communication with a central clockc. Time stamps sensor data captured by the sensor data generation moduled. Used for correlating/syncing sensor data captured by multiple recording devicese. Time stamps content captured by the content capturing modulef. Used for syncing various content streams5. The Location Modulea. Enables the reading and communicating of location data associated with a sensing device;b. The location data may be obtained by way of, for example, but not limited to:i. GPS/IP Address/Triangulationii. LAN/WAN6. The Orientation Modulea. Enables the capture of orientation data from multiple sensing devices associated with the content capture module7. The Communications Modulea. Enables the networking of the multiple sensor data generation modules associated with multiple networked devicesb. Configured to join ‘groups’ of devices generating sensor data under a similar context (e.g. location/theme/etc.)c. Remote control of the sensor data generation modulesd. Enables the networking of the multiple content capture modules associated with multiple networked devicese. In operative communication with other communications modules of computing devices capturing contentf. Configured to communicate with nearby devices also running on the platformg. Configured to join ‘groups’ of devices capturing content under a similar ‘location/theme/etc.’h. Remote control of the capturing modulesi. Remote control of the cameraj. Remote control of the microphonek. Via Wireless Medial. Via Wired Media

Various hardware components may be used at the various stages of operations follow the method and computer-readable medium. For example, although the methods have been described to be performed by a computing device, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with the computing device. For example, server110and/or computing device1500may be employed in the performance of some or all of the stages disclosed with regard to the methods below.

III. Embodiments of the present disclosure provide a hardware and software platform operative by a set of methods and computer-readable media comprising instructions configured to operate the aforementioned modules and computing elements in accordance with the methods.

The methods and computer-readable media may comprise a set of instructions which when executed are configured to enable a method for inter-operating at least one of the following modules:A. Sensor Data Generation Module;B. Sensor Data Reception Module;C. ROI Estimation Module; andD. ROI Display Module

The aforementioned modules may be inter-operated to perform a method comprising the following stages:1. Receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices;a. wherein the plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices;i. wherein the sensor data comprises information indicative of a plurality of viewpoints respective to the ROI, the plurality of viewpoints respective to the ROI is respectively associated with the plurality of content streams;ii. wherein sensor data comprises imagery information (e.g., data, metadata, and/or signaling, etc.);b. wherein each of the plurality of mobile devices comprises at least one recorder configured to capture a corresponding content steam;c. wherein a sensor data associated with a mobile device comprises a location data and an orientation data of the mobile device during capturing of a content stream;i. Receiving, by the system, a plurality of optical characteristics corresponding to the plurality of mobile devices; and wherein determining the ROI is based at least on the plurality of optical characteristics;1. wherein an optical characteristic of a mobile device comprises a Field of View (FOV) of a lens comprised in the mobile device;2. receiving a plurality of device identifiers from the plurality of mobile devices, wherein the plurality of optical characteristics is identified based on at least the plurality of device identifiers;3. wherein the device identifier comprises at least one of a device serial number, device identification information, device manufacturer, device type, device model, device component information and a version of software associated with the device;2. Analyzing, by the system, the plurality of sensor data;a. wherein analyzing the plurality of sensor data comprises:i. determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices; andii. determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices, a vector associated with an origin point originating at the origin point;3. Determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI;a. wherein determining the ROI is based at least on the plurality of origin points and the plurality of vectors;b. wherein the plurality of origin points is configured to be used as coordinates in a mapping coordinate system;c. further comprising determining a plurality of vector intersections of the plurality of vectors;d. wherein determining the ROI is based at least on the plurality of vector intersections;e. wherein an attribute associated with the ROI comprises a shape, a size, a spatial dimension, a position, an orientation, and a volume;f. wherein determining the ROI comprises determining at least one spatial dimension of the ROI;g. wherein the at least one depth is estimated based at least on the origin point closest to the ROI;h. further comprising determining a center point of the ROI based at least on the plurality of origin points;i. wherein the plurality of distances is computed based on the center point, wherein a distance of the plurality of distances corresponding to an origin point is between the center point and the origin point;j. further comprising sampling a plurality of directions associated with the ROI;k. wherein a shape of the ROI is estimated based on closest origin point from the center point in each direction of the plurality of directions;l. wherein the at least one object is characterized by at least one of a size, a shape, a volume, a position, an orientation and a spatial dimension;m. wherein determining the ROI comprises calculating at least one of a two-dimensional volume and a three-dimensional volume;

n. Receiving a plurality of Field of Views (FOVs) associated with the plurality of mobile devices, a Field of View (FOV) associated with a mobile device being associated with a lens comprised in the mobile device;o. Determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices, the plurality of origin points configured as coordinates in a mapping coordinate system;p. Determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices, a vector associated with an origin point originating at the origin point;i. wherein determining a plurality of origin points comprises converting a plurality of location data corresponding to the plurality of mobile devices into a plurality of origin points in a mapping coordinate system; and wherein determining a plurality of vectors comprises converting a plurality of orientation data corresponding to the plurality of mobile devices into a plurality of vectors associated with the plurality of origin points, wherein a vector associated with an origin point originates at the origin point;q. Generating a plurality of viewing frustums based at least on the plurality of FOVs, wherein an apex of a viewing frustum associated with a mobile device is coincident with the origin point associated with the mobile device, wherein a view direction of the viewing frustum is aligned with the vector associated with the mobile device; andr. Determining a plurality of intersections of the plurality of viewing frustums, wherein determining the ROI is based at least on the plurality of intersections of the plurality of viewing frustums;

s. Receiving the plurality of content streams from the mobile devices;t. Analyzing the plurality of content streams;u. Determining at least one object associated with the ROI; andv. Determining at least one spatial dimension associated with the at least one object; wherein determining the ROI is based at least on the at least one spatial dimension;

w. Receiving the plurality of content streams from the mobile devices;x. Analyzing the plurality of content streams;y. Determining at least one object associated with the ROI based at least on the plurality of content streams;z. Determining at least one spatial dimension associated with the at least one object based at least on a predetermined association between a plurality of objects and a plurality of spatial dimensions;aa. Determining a plurality of distances corresponding to the plurality of mobile devices and the ROI, wherein a distance between a mobile device and the ROI is estimated based on a spatial dimension of the at least one object determined in a content stream captured by the mobile device; wherein determining the ROI is based at least on the plurality of distances;i. wherein determining the plurality of distances comprises determining a plurality of distances corresponding to the plurality of mobile devices;ii. wherein a distance between a mobile device and the ROI is determined based at least on a spatial dimension of the at least one object determined in a content stream captured by the mobile device;iii. wherein the sensor data further comprises a distance data representing a distance of the mobile device from at least one object comprised in the ROI, wherein the mobile device comprises a ranging sensor configured to determine the distance;iv. wherein the ranging sensor comprises at least one of a LIDAR sensor and an image sensor;

bb. executing a search on a venue database based on the location data;cc. determining the venue from a venue database based at least on the location data; anddd. retrieving at least one spatial characteristic of the venue;ee. wherein determining the ROI is based at least on the at least one spatial characteristic.

ff. wherein the plurality of mobile devices comprises a drone configured for flight.gg. further comprising determining at least one trajectory associated with at least one mobile device based at least on analysis of the plurality of sensor data; wherein determining the ROI is based at least on the at least one trajectory.4. Generating a visualization of the ROI and the plurality of mobile devices;a. transmitting the visualization to at least one mobile device of the plurality of mobile devices;b. receiving a selection associated with the visualization;c. identifying one or more mobile devices associated with the selection;d. transmitting one or more content streams captured by the one or more mobile devices;

Although the stages are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Stages may be combined, separated, reordered, and various intermediary stages may exist. Accordingly, it should be understood that the various stages, in various embodiments, may be performed in arrangements that differ from the ones claimed below. Moreover, various stages may be added or removed from the without altering or deterring from the fundamental scope of the depicted methods and systems disclosed herein.

IV. Embodiments of the present disclosure provide a hardware and software platform operative as a distributed system of modules and computing elements. The system comprising:A. Sensor Data Generation Module configured for:a. Generation of sensor datai. Location dataii. Orientation dataiii. Motion dataiv. Speed dataB. Sensor Data Reception Module configured for:a. Receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices;I. wherein the plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices;i. wherein the sensor data comprises information indicative of a plurality of viewpoints respective to the ROI, the plurality of viewpoints respective to the ROI is respectively associated with the plurality of content streams;ii. wherein sensor data comprises imagery information (e.g., data, metadata, and/or signaling, etc.);II. wherein each of the plurality of mobile devices comprises at least one recorder configured to capture a corresponding content steam;III. wherein a sensor data associated with a mobile device comprises a location data and an orientation data of the mobile device during capturing of a content stream;i. Receiving, by the system, a plurality of optical characteristics corresponding to the plurality of mobile devices; and wherein determining the ROI is based at least on the plurality of optical characteristics;1. wherein an optical characteristic of a mobile device comprises a Field of View (FOV) of a lens comprised in the mobile device;2. receiving a plurality of device identifiers from the plurality of mobile devices, wherein the plurality of optical characteristics is identified based on at least the plurality of device identifiers;3. wherein the device identifier comprises at least one of a device serial number, device identification information, device manufacturer, device type, device model, device component information and a version of software associated with the device;C. ROI Estimation Module configured for:a. Determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI;i. wherein determining the ROI is based at least on the plurality of origin points and the plurality of vectors;ii. wherein the plurality of origin points is configured to be used as coordinates in a mapping coordinate system;iii. further comprising determining a plurality of vector intersections of the plurality of vectors;iv. wherein determining the ROI is based at least on the plurality of vector intersections;v. wherein an attribute associated with the ROI comprises a shape, a size, a spatial dimension, a position, an orientation, and a volume;vi. wherein determining the ROI comprises determining at least one spatial dimension of the ROI;vii. wherein the at least one depth is estimated based at least on the origin point closest to the ROI;viii. further comprising determining a center point of the ROI based at least on the plurality of origin points;ix. wherein the plurality of distances is computed based on the center point, wherein a distance of the plurality of distances corresponding to an origin point is between the center point and the origin point;x. further comprising sampling a plurality of directions associated with the ROI;xi. wherein a shape of the ROI is estimated based on closest origin point from the center point in each direction of the plurality of directions;xii. wherein the at least one object is characterized by at least one of a size, a shape, a volume, a position, an orientation and a spatial dimension;xiii. wherein determining the ROI comprises calculating at least one of a two-dimensional volume and a three-dimensional volume;D. ROI Display Module configured for:a. Generating a visualization of the ROI and the plurality of mobile devices;b. transmitting the visualization to at least one mobile device of the plurality of mobile devices;c. receiving a selection associated with the visualization;d. identifying one or more mobile devices associated with the selection;e. transmitting one or more content streams captured by the one or more mobile devices;

According to some embodiments, disclosed herein is a method of estimating a Region Of Interest (ROI) corresponding to a plurality of content streams. The method may include receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices. The plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices. Further, each of the plurality of mobile devices may include at least one recorder configured to capture a corresponding content steam. Further, a sensor data associated with a mobile device may include a location data and an orientation data of the mobile device during capturing of a content stream. The method may further include, analyzing, by the system, the plurality of sensor data. Additionally, the method may include determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI.

In some embodiments, the method may further include receiving, by the system, a plurality of optical characteristics corresponding to the plurality of mobile devices. Accordingly, determining the ROI may be based at least on the plurality of optical characteristics. In some embodiments, an optical characteristic of a mobile device may include a Field of View (FOV) of a lens comprised in the mobile device. In some embodiments, the method may further include receiving a plurality of device identifiers from the plurality of mobile devices. Further, the plurality of optical characteristics may be identified based on at least the plurality of device identifiers.

In some embodiments, analyzing the plurality of sensor data may include determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices. Further, analyzing the plurality of sensor data may include determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices. A vector associated with an origin point may originate at the origin point.

In some embodiments, determining the ROI may be based at least on the plurality of origin points and the plurality of vectors. In some embodiments, the plurality of origin points may be configured to be used as coordinates in a mapping coordinate system. In some embodiments, the method may further include determining a plurality of vector intersections of the plurality of vectors. In some embodiments, determining the ROI may be based at least on the plurality of vector intersections.

In some embodiments, the method may further include receiving a plurality of Field of Views (FOVs) associated with the plurality of mobile devices. A Field of View (FOV) associated with a mobile device may be associated with a lens comprised in the mobile device. Additionally, the method may further include determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices. The plurality of origin points may be configured as coordinates in a mapping coordinate system. Additionally, the method may include determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices. A vector associated with an origin point may originate at the origin point. Further, the method may include generating a plurality of viewing frustums based at least on the plurality of FOVs. Furthermore, an apex of a viewing frustum associated with a mobile device may be coincident with the origin point associated with the mobile device. Further, a view direction of the viewing frustum may be aligned with the vector associated with the mobile device. Additionally, the method may include determining a plurality of intersections of the plurality of viewing frustums. Further, determining the ROI may be based at least on the plurality of intersections of the plurality of viewing frustums.

In some embodiments, determining a plurality of origin points may include converting a plurality of location data corresponding to the plurality of mobile devices into a plurality of origin points in a mapping coordinate system. Further, determining a plurality of vectors may include converting a plurality of orientation data corresponding to the plurality of mobile devices into a plurality of vectors associated with the plurality of origin points. Further, a vector associated with an origin point may originate at the origin point.

In some embodiments, the method may further include receiving the plurality of content streams from the mobile devices. Additionally, the method may include analyzing the plurality of content streams. Further, the method may include determining at least one object associated with the ROI. Furthermore, the method may include determining at least one spatial dimension associated with the at least one object. Accordingly, determining the ROI may be based at least on the at least one spatial dimension.

In some embodiments, the method may further include receiving the plurality of content streams from the mobile devices. Further, the method may include analyzing the plurality of content streams. Additionally, the method may include determining at least one object associated with the ROI based at least on the plurality of content streams. Furthermore, the method may include determining at least one spatial dimension associated with the at least one object based at least on a predetermined association between a plurality of objects and a plurality of spatial dimensions. Additionally, the method may include determining a plurality of distances corresponding to the plurality of mobile devices and the ROI. Further, a distance between a mobile device and the ROI may be estimated based on a spatial dimension of the at least one object determined in a content stream captured by the mobile device. Accordingly, determining the ROI may be based at least on the plurality of distances.

In some embodiments, determining the plurality of distances may include determining a plurality of distances corresponding to the plurality of mobile devices. Further, a distance between a mobile device and the ROI may be determined based at least on a spatial dimension of the at least one object determined in a content stream captured by the mobile device.

In some embodiments, the sensor data further may include a distance data representing a distance of the mobile device from at least one object comprised in the ROI. Further, the mobile device may include a ranging sensor configured to determine the distance. In some embodiments, the ranging sensor may include a LIDAR sensor, an image sensor, or a combination thereof.

In some embodiments, the method may further include executing a search on a venue database based on the location data. Further, the method may include determining the venue from a venue database based at least on the location data. Additionally, the method may include retrieving at least one spatial characteristic of the venue. Accordingly, determining the ROI may be based at least on the at least one spatial characteristic.

In some embodiments, the plurality of mobile devices may include a drone configured for flight. In some embodiments, the method may further include determining at least one trajectory associated with at least one mobile device based at least on analysis of the plurality of sensor data. Accordingly, determining the ROI may be based at least on the at least one trajectory.

In some embodiments, the method may further include generating a visualization of the ROI and the plurality of mobile devices. Additionally, the method may include transmitting the visualization to at least one mobile device of the plurality of mobile devices. In some embodiments, the method may further include receiving a selection associated with the visualization. Further, the method may include identifying one or more mobile devices associated with the selection. Furthermore, the method may include transmitting one or more content streams captured by the one or more mobile devices.

II. PLATFORM CONFIGURATION

FIG. 1Ais an illustration of a platform consistent with various embodiments of the present disclosure. The online platform100may include a content capturing module and a sensor data generation module125. The content capturing and sensor data generation module may include a plurality of recording devices, such as, but not limited to, a mobile device with camera, a camera drone, a tripod mounted camera, CCTV cameras and so on. Further, the plurality of recording devices may further include one or more sensors, such as but not limited to, location sensor, orientation sensor, motion sensor, speed sensor, direction of travel sensor and so on. Accordingly, the content capturing module and sensor data generation module125may be configured to generate and transmit multiple streams of sensor data corresponding to the plurality of recording devices. Similarly, the content capturing module and sensor data generation module125may be configured to generate and transmit multiple streams of content (e.g. video, audio etc.) captured by the plurality of recording devices.

Further, the platform100may include a content transmission module135configured to communicate with the content capturing and sensor data generation module125over a communication network112. Accordingly, the content transmission module135may be configured to receive multiple streams of sensor data and/or content transmitted by the content capturing and sensor data generation module125.

Furthermore, the platform100may include an ROI estimation module145configured to estimate ROI based on the multiple streams of sensor data and/or content transmitted by the content capturing and sensor data generation module125.

Additionally, the platform100may include an ROI display module155configured for generating a visual display of the ROI. For example, the ROI display module155may be comprised in an electronic device (such as a smartphone, a laptop computer, a tablet computer, a desktop computer, a wearable computer etc.) operated by a user105.

By way of non-limiting example, the online platform100for estimating ROI based on multiple streams of sensor data may be hosted on a centralized server110, such as, for example, a cloud computing service. The centralized server may communicate with other network entities, such as, for example, a plurality of mobile devices120A, electronic devices120B (such as desktop computers, laptop computers etc.) and one or more recording devices120C, (e.g., smartphone cameras, digital cameras, camera drones120D etc.) over a communication network112, such as, but not limited to, the Internet. Further, users of the platform may include, but are not limited to, content creators115(e.g. live streamers), content consumers105(e.g. viewers/listeners of live streams) and platform managers (e.g. administrator or moderator). Accordingly, electronic devices operated by the content creators, content consumers and platform managers may be in communication with the platform.

A user, such as a content creator115, a content consumer105or a moderator, may access platform100through a software application. The software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device1500. One possible embodiment of the software application may be provided by Shouty-Live™ products and services.

Accordingly, in an instance, the user115may be a content creator. Accordingly, an electronic device, such as recording device120C, operated by the user115, may be configured to generate sensor data (e.g. location data and orientation data) and capture content (e.g. audio, video etc.). Accordingly, the recording device may include one or more of a location sensor for sensing geolocation, an orientation sensor for sensing orientation of a mobile device along one or more axis, a ranging sensor for sensing distance of an object from the mobile device, a sound sensor for sensing sound, a light sensor for sensing light intensity, a motion sensor for sensing movement of the mobile device, an accelerometer and one or more environmental sensors for sensing environmental variables (e.g. temperature, humidity, etc.). For example, the user115may be a member of the audience in a music concert recording and live-streaming the music concert. Accordingly, a location of the user115(i.e. a geographical location) and orientation of the recording device120C may be regularly determined and transmitted as a stream of sensor data. Further, the user115may provide event information and/or venue information in addition to capturing content (audio and/or video).

In yet another instance, the user105may be a content consumer. Accordingly, the user105may provide a selection input based on which one or more of content streams may be selected and presented to the user105. Further, the user105may provide user selections on a visualization of ROI including positions of various recording devices (i.e. smartphone cameras, tripod mounted cameras, drone cameras etc.) in relation to the ROI. Accordingly, the content streams captured by recording devices corresponding to the user selection may be retrieved and transmitted to the electronic device operated by the user105. Additionally, the user105may provide a search query using metadata such as location data, orientation data, etc. and retrieve content streams corresponding to the specified metadata.

As will be detailed with reference toFIG. 15below, the computing device through which the ROI estimation platform may be accessed may comprise, but not be limited to, for example, a desktop computer, laptop, a tablet, or mobile telecommunications device. As will be detailed with reference toFIG. 15below, the computing device through which the platform may be accessed may comprise, but not be limited to, for example, a desktop computer, laptop, a tablet, or mobile telecommunications device. Though the present disclosure is written with reference to a mobile telecommunications device, it should be understood that any computing device may be employed to provide the various embodiments disclosed herein.

FIG. 1Billustrates various system modules, namely, content capturing and sensor data generation module125, ROI estimation module145, ROI display module155and content transmission module135. As shown, the content transmission module135mediates communication between the system modules.

FIG. 2illustrates a top view of a plurality of users recording an event using an associated plurality of recording devices, in accordance with some embodiments. The event may be occurring on an area202(such as a stage of a concert hall). Accordingly, a plurality of users204(such as204a,204band204c) may be situated at different locations in relation to the area202. Further, the plurality of users204may be recording the event with a plurality of recording devices (such as206a,206band206c). Examples of recording devices may include, but are not limited to, smartphone cameras, digital cameras, IP cameras, tripod mounted cameras, CCTV cameras, sound recording devices, etc. Furthermore, the recording devices may include one or more sensors such as, but not limited to, location sensor (e.g. GPS receiver), orientation sensor (e.g. electronic compass), a motion sensor (e.g. accelerometer), a speed sensor etc. As a result, the recording devices206may generate a stream of sensor data and/or a stream of content (e.g. audio, video, multimedia). Further, such streams of sensor data and/or content may be time-stamped based on a standard time base (e.g. time provided by cellular network provider). Alternatively, each of the plurality of recording devices206may include an application configured to receive a synchronization data in order to synchronize a local timer with an external standard timer (e.g. a timer on a server). As a result, the stream of sensor data and/or content may be correlated in time. In other words, a given time instant, sensor data from each of the plurality of recording devices206may be identified and collected.

The area202is an example of a Region Of Interest (ROI) as referred to in the present disclosure. Further, the methods and systems disclosed herein estimate the ROI based on the stream of sensor data and/or content captured by the plurality of recording devices206. In an instance, the ROI is estimated based on locations of the plurality of recording devices206and the orientations of the plurality of recording devices206. Accordingly, location and orientation may be represented geometrically in the form of origin points and vectors for facilitating estimation of the ROI.

Specifically,FIG. 3illustrates construction of origin points corresponding to locations of the mobile devices and vectors corresponding to the orientation of the recording devices for ROI estimation, in accordance with some embodiments. Based on location data corresponding to the plurality of recording devices206, a plurality of origin points302may be constructed. For example, origin point302acorresponding to location of recording device206a, origin point302bcorresponding to location of recording device206band origin point302ccorresponding to location of recording device206cmay be constructed. In an instance, the location data may be two-dimensional and include latitude and longitude. In another instance, the location may be three-dimensional and accordingly include latitude, longitude and altitude. Accordingly, a corresponding coordinate space (i.e. 2D or 3D) may be constructed. Further, co-ordinates of the plurality of origin points302may be identical to the corresponding latitude, longitude and altitude values of the location data. Alternatively, in an instance, the latitude, longitude and altitude values may be normalized in order to generate the co-ordinates of the plurality of origin points302. In other words, a spatial frame of reference for the plurality of origin points302may be different from that of the location data.

Further, based on orientation data corresponding to the plurality of recording devices206, a plurality of vectors304may be constructed. For example, vector304arepresenting the orientation of recording device206a, vector304brepresenting the orientation of recording device206band vector304crepresenting the orientation of recording device206cmay be constructed. Further, based on the dimensionality of the location data, the plurality of vectors304may be 2D or 3D. Furthermore, in some instances, the dimensionality of both the plurality of origin points302and the plurality of vectors304may be less than the dimensionality of the location data and the orientation data.

Further, the plurality of vectors304may be extended to allow for intersection between the plurality of vectors304. Based on the intersection points between the plurality of vectors304, the ROI may be estimated. For example, a high spatial density of intersection points may be an approximate indication of the ROI. However, in order to determine the ROI with greater accuracy, field of views of the plurality of recording devices206may be taken into account.

Accordingly,FIG. 4illustrates ROI estimation based on construction of origin points corresponding to locations of the recording devices, vectors corresponding to the orientation of the recording devices and 2D viewing frustums centered on the origin points, in accordance with some embodiments. As illustrated, a plurality of viewing frustums402corresponding to the plurality of recording devices206may be constructed in the coordinate space. For example, viewing frustum402acorresponding to recording device206a, viewing frustum402bcorresponding to recording device206band viewing frustum402ccorresponding to recording device206cmay be constructed based on optical characteristics, such as, but not limited to, Field of View (FOV) of lens comprised in recording device206a,206band206crespectively. Accordingly, the FOV values of the recording devices206may be determined and transmitted by the recording devices206along with location data and orientation data. Alternatively, the recording devices206may be configured to transmit device information (e.g. model number, product code etc.) which may enable the platform100to automatically identify the corresponding FOV values by querying a device information database.

Accordingly, based on a FOV value of a recording device206, a corresponding viewing frustum402may be constructed centered on the corresponding origin point302and oriented along the corresponding vector304. For example, viewing frustum402may be constructed with the center point proximal to the origin point302awhile the orientation of the viewing frustum402is along the direction of the vector304a.

Subsequently, the viewing frustums402may be extended to allow for intersections. Accordingly, a plurality of intersections404(e.g.404a-e) between the viewing frustums402may be formed. Thereafter, by interconnecting intersection points404lying on the periphery of a cluster of intersection points404, the ROI406may be estimated. In some embodiments, certain intersection points404may be excluded from consideration by taking into account prior knowledge about spatial characteristics of a venue where the plurality of recording devices are situated. Additionally, in some embodiments, the ROI may be approximated based on closest match to a predetermined shapes of areas (e.g. rectangular, spherical, elliptical, etc.).

FIG. 5illustrates ROI estimated based on construction of origin points corresponding to locations of the recording devices, vectors corresponding to the orientation of the recording devices and viewing frustums centered on the origin points, in accordance with some embodiments. As illustrated, the ROI502may be determined based on spatial distribution of intersection points corresponding to intersection of the plurality of viewing frustums402.

FIG. 6illustrates estimation of ROI based on calculating a distance between a recording device and an object within the ROI using image analysis, in accordance with some embodiments. As shown, the event may be taking place in a physical space602involving one or more objects604(e.g. a person). Further, the object may be associated with a spatial dimension606, such as, for example, height. When the event is captured by a recording device206(e.g.206a), an image608of the object604may be formed. Further, a spatial dimension610of the image object604may be determined using image analysis. Moreover, by using image analysis, the real world object corresponding to the image object604may be determined. In other words, image analysis may be performed to identify one or more objects being captured by the recording device206. As a result, based on prior knowledge about spatial characteristics of various objects, the platform may calculate a distance612between the recording device206aand the object604. For instance, once the object604is detected to be a human, a predetermined average height of the human may be used in computing the distance612.

Accordingly, distances between recording devices206and one or more objects604in the ROI may be computed. These distances may then be taken into account in estimating the ROI. In particular, by utilizing these distances, a shape and/or size of the ROI may be calculated more accurately.

FIG. 7illustrates estimation of ROI based on calculating a plurality of distances between a plurality of recording devices and an object within the ROI using image analysis, in accordance with some embodiments. As illustrated, the event may involve a physical space such as a stage upon which one or more objects702(e.g. persons702aand702b) may be performing. Further, the event may be captured by multiple recording devices, such as, recording devices206a,206band206c. Additionally, by utilizing image analysis (as explained in conjunction withFIG. 6), a plurality of distances704(e.g.704a,704band704c) may be computed between the plurality of recording devices206and the one or more objects702. Subsequently, by taking into account the plurality of distances704, a shape and/or size of the ROI may be calculated more accurately.

FIG. 8illustrates construction of origin points corresponding to locations of the recording devices, vectors corresponding to the orientation of the recording devices and 3D viewing frustums centered on the origin points for ROI estimation, in accordance with some embodiments. As illustrated, a plurality of 3D viewing frustums802(e.g.802a,802band802c) may be constructed corresponding to the plurality of recording devices206. Further, each 3D viewing frustum may be centered co-incident with a corresponding origin point. For example, 3D viewing frustum802ais constructed with its center point coincident with the origin point302aassociated with the recording device206a. In other words, an apex of a 3D viewing frustum802associated with a recording device206may be coincident with an origin point302associated with the corresponding recording device206. Furthermore, a view direction of the 3D viewing frustum802may be aligned with the corresponding vector304associated with the origin point302. Subsequently, a plurality of intersections of multiple viewing frustums802may be determined. Such determination may be performed computationally using coordinate geometry. Further, by computationally analyzing characteristics of the plurality of intersections of viewing frustums802, the ROI may be estimated.

III. PLATFORM OPERATION

Although methods900to1400have been described to be performed by platform100, it should be understood that computing device1500may be used to perform the various stages of methods900to1400. Furthermore, in some embodiments, different operations may be performed by different networked elements in operative communication with computing device1500. For example, server110may be employed in the performance of some or all of the stages in methods900to1400. Moreover, server110may be configured much like computing device1500.

Although the stages illustrated by the flow charts are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Stages may be combined, separated, reordered, and various intermediary stages may exist. Accordingly, it should be understood that the various stages illustrated within the flow chart may be, in various embodiments, performed in arrangements that differ from the ones illustrated. Moreover, various stages may be added or removed from the flow charts without altering or deterring from the fundamental scope of the depicted methods and systems disclosed herein. Ways to implement the stages of methods900to1400will be described in greater detail below.

FIG. 9illustrates a flowchart of a method900of estimating ROI based on sensor data captured by a plurality of mobile devices and generating a visualization of the ROI including positions of the plurality of mobile devices, in accordance with some embodiments. The method900may include a stage902of receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices. The plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices. Further, each of the plurality of mobile devices may include at least one recorder configured to capture a corresponding content steam. Further, a sensor data associated with a mobile device may include a location data and an orientation data of the mobile device during capturing of a content stream.

In some embodiments, the method may further include receiving, by the system, a plurality of optical characteristics corresponding to the plurality of mobile devices. Accordingly, determining the ROI may be based at least on the plurality of optical characteristics. In some embodiments, an optical characteristic of a mobile device may include a Field of View (FOV) of a lens comprised in the mobile device. In some embodiments, the method may further include receiving a plurality of device identifiers from the plurality of mobile devices. Further, the plurality of optical characteristics may be identified based on at least the plurality of device identifiers.

The method900may further include a stage904of analyzing, by the system, the plurality of sensor data. In some embodiments, analyzing the plurality of sensor data may include determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices. Further, analyzing the plurality of sensor data may include determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices. A vector associated with an origin point may originate at the origin point.

Additionally, the method900may include a stage904of determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI. In some embodiments, determining the ROI may be based at least on the plurality of origin points and the plurality of vectors. In some embodiments, the plurality of origin points may be configured to be used as coordinates in a mapping coordinate system. In some embodiments, the method may further include determining a plurality of vector intersections of the plurality of vectors. In some embodiments, determining the ROI may be based at least on the plurality of vector intersections.

Furthermore, the method900may include a stage906of generating a visualization of the ROI and the plurality of mobile devices.

In some embodiments, the plurality of mobile devices may include a drone configured for flight. In some embodiments, the method may further include determining at least one trajectory associated with at least one mobile device based at least on analysis of the plurality of sensor data. Accordingly, determining the ROI may be based at least on the at least one trajectory.

FIG. 10illustrates a flowchart of a method1000of estimating ROI based on construction of origin points corresponding to locations of the mobile devices, vectors corresponding to the orientation of the mobile devices, viewing frustums centered on the origin points and intersections between the viewing frustums, in accordance with some embodiments. Accordingly, the method1000may include a stage1002of receiving a plurality of Field of Views (FOVs) associated with the plurality of mobile devices. A Field of View (FOV) associated with a mobile device may be associated with a lens comprised in the mobile device. Additionally, the method1000may include a stage1004of determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices. The plurality of origin points may be configured as coordinates in a mapping coordinate system. Additionally, the method1000may include a stage1006odetermining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices. A vector associated with an origin point may originate at the origin point. In some embodiments, determining a plurality of origin points may include converting a plurality of location data corresponding to the plurality of mobile devices into a plurality of origin points in a mapping coordinate system. Further, determining a plurality of vectors may include converting a plurality of orientation data corresponding to the plurality of mobile devices into a plurality of vectors associated with the plurality of origin points. Further, a vector associated with an origin point may originate at the origin point.

Further, the method1000may include a stage1008of generating a plurality of viewing frustums based at least on the plurality of FOVs. Furthermore, an apex of a viewing frustum associated with a mobile device may be coincident with the origin point associated with the mobile device. Further, a view direction of the viewing frustum may be aligned with the vector associated with the mobile device. Additionally, the method1000may include a stage1010of determining a plurality of intersections of the plurality of viewing frustums. Further, determining the ROI may be based at least on the plurality of intersections of the plurality of viewing frustums.

FIG. 11illustrates a flowchart of a method1100of estimating ROI based on image analysis of content streams captured by a plurality of mobile devices, in accordance with some embodiments. Accordingly, the method1100may include a stage1102of receiving the plurality of content streams from the mobile devices. Additionally, the method1100may include a stage1104of analyzing the plurality of content streams. Further, the method1100may include a stage1106of determining at least one object associated with the ROI. Furthermore, the method1100may include a stage1108of determining at least one spatial dimension associated with the at least one object. Accordingly, determining the ROI may be based at least on the at least one spatial dimension.

FIG. 12illustrates a flowchart of a method1200of estimating ROI based on image analysis of content streams captured by a plurality of mobile devices to calculate distances between the plurality of mobile devices and an object within the ROI, in accordance with some embodiments. Accordingly, the method1200may include a stage1202of receiving the plurality of content streams from the mobile devices. Further, the method1200may include a stage1204of analyzing the plurality of content streams. Additionally, the method1200may include a stage1206of determining at least one object associated with the ROI based at least on the plurality of content streams. Furthermore, the method1200may include a stage1208of determining at least one spatial dimension associated with the at least one object based at least on a predetermined association between a plurality of objects and a plurality of spatial dimensions. Additionally, the method1200may include a stage1210of determining a plurality of distances corresponding to the plurality of mobile devices and the ROI. Further, a distance between a mobile device and the ROI may be estimated based on a spatial dimension of the at least one object determined in a content stream captured by the mobile device. Accordingly, determining the ROI may be based at least on the plurality of distances.

In some embodiments, determining the plurality of distances may include determining a plurality of distances corresponding to the plurality of mobile devices. Further, a distance between a mobile device and the ROI may be determined based at least on a spatial dimension of the at least one object determined in a content stream captured by the mobile device.

In some embodiments, the sensor data further may include a distance data representing a distance of the mobile device from at least one object comprised in the ROI. Further, the mobile device may include a ranging sensor configured to determine the distance. In some embodiments, the ranging sensor may include a LIDAR sensor, an image sensor, or a combination thereof.

FIG. 13illustrates a flowchart of a method1300of estimating ROI based on predetermined spatial characteristics of a venue, in accordance with some embodiments. Accordingly, the method1300may include a stage1302of executing a search on a venue database based on the location data. Further, the method1300may include a stage1304of determining the venue from a venue database based at least on the location data. Additionally, the method may include a stage1306of retrieving at least one spatial characteristic of the venue. Further, the method1300may include a stage1308of determining the ROI based at least on the at least one spatial characteristic.

FIG. 14illustrates a flowchart of a method1400of transmitting content streams based on user selections on a visualization of an ROI including multiple mobile devices, in accordance with some embodiments. Accordingly, the method1400may include a stage1402of transmitting the visualization to at least one mobile device of the plurality of mobile devices. Further, the method1400may further include a stage1404of receiving a selection associated with the visualization. Furthermore, the method1400may include a stage1406of identifying one or more mobile devices associated with the selection. Additionally, the method1400may include a stage1408of transmitting one or more content streams captured by the one or more mobile devices.

IV. PLATFORM ARCHITECTURE

The platform100may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device. The computing device may comprise, but not be limited to, a desktop computer, laptop, a tablet, or mobile telecommunications device. Moreover, the platform100may be hosted on a centralized server, such as, for example, a cloud computing service. Although methods900to1400have been described to be performed by a computing device1500, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with computing device1500.

Embodiments of the present disclosure may comprise a system having a memory storage and a processing unit. The processing unit coupled to the memory storage, wherein the processing unit is configured to perform the stages of methods900to1400.

FIG. 15is a block diagram of a system including computing device1500. Consistent with an embodiment of the disclosure, the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device1500ofFIG. 15. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit. For example, the memory storage and processing unit may be implemented with computing device1500or any of other computing devices1518, in combination with computing device1500. The aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the disclosure.

With reference toFIG. 15, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device1500. In a basic configuration, computing device1500may include at least one processing unit1502and a system memory1504. Depending on the configuration and type of computing device, system memory1504may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory1504may include operating system1505, one or more programming modules1506, and may include a program data1507. Operating system1505, for example, may be suitable for controlling computing device1500's operation. In one embodiment, programming modules1506may include the following modules: User Interface (UI) module, Sensor Data Generation Module (or Sensor Module), Timing Module, Content Capturing module (or Capture Module) and Communications (Com) Module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG. 15by those components within a dashed line1508.

Computing device1500may have additional features or functionality. For example, computing device1500may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG. 15by a removable storage1509and a non-removable storage1510. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory1504, removable storage1509, and non-removable storage1510are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device1500. Any such computer storage media may be part of device1500. Computing device1500may also have input device(s)1512such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s)1514such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

As stated above, a number of program modules and data files may be stored in system memory1504, including operating system1505. While executing on processing unit1502, programming modules1506(e.g., application1520) may perform processes including, for example, stages of one or more of methods900to1400as described above. The aforementioned process is an example, and processing unit1502may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include image encoding applications, machine learning application, image classifiers etc.

According to various embodiments, the present disclosure includes at least the following aspects:

Aspect 1: A method of estimating a Region Of Interest (ROI) corresponding to a plurality of content streams, the method comprising: receiving, by a system having at least one processor, a plurality of sensor data associated with a plurality of mobile devices, wherein the plurality of sensor data corresponds to a plurality of content streams captured by the plurality of mobile devices, wherein each of the plurality of mobile devices comprises at least one recorder configured to capture a corresponding content steam, wherein a sensor data associated with a mobile device comprises a location data and an orientation data of the mobile device during capturing of a content stream;

analyzing, by the system, the plurality of sensor data; and

determining, by the system, a ROI based on at least the plurality of sensor data, thereby generating an estimation of the ROI.

Aspect 2: The method of aspect 1, further comprising receiving, by the system, a plurality of optical characteristics corresponding to the plurality of mobile devices; and wherein determining the ROI is based at least on the plurality of optical characteristics.

Aspect 3: The method of aspect 2, wherein an optical characteristic of a mobile device comprises a Field of View (FOV) of a lens comprised in the mobile device.

Aspect 4: The method of aspect 2, further comprising receiving a plurality of device identifiers from the plurality of mobile devices, wherein the plurality of optical characteristics is identified based on at least the plurality of device identifiers.

Aspect 5: The method of aspect 1, wherein analyzing the plurality of sensor data comprises: determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices; and determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices, a vector associated with an origin point originating at the origin point.

Aspect 6: The method of aspect 5, wherein determining the ROI is based at least on the plurality of origin points and the plurality of vectors.

Aspect 7: The method of aspect 6, wherein the plurality of origin points is configured to be used as coordinates in a mapping coordinate system.

Aspect 8: The method of aspect 7, further comprising determining a plurality of vector intersections of the plurality of vectors,

Aspect 9: The method of aspect 8, wherein determining the ROI is based at least on the plurality of vector intersections.

Aspect 10: The method of aspect 1, further comprising: receiving a plurality of Field of Views (FOVs) associated with the plurality of mobile devices, a Field of View (FOV) associated with a mobile device being associated with a lens comprised in the mobile device;

determining a plurality of origin points based at least on a plurality of location data corresponding to the plurality of mobile devices, the plurality of origin points configured as coordinates in a mapping coordinate system;

determining a plurality of vectors associated with the plurality of origin points based at least on a plurality of orientation data corresponding to the plurality of mobile devices, a vector associated with an origin point originating at the origin point;

generating a plurality of viewing frustums based at least on the plurality of FOVs, wherein an apex of a viewing frustum associated with a mobile device is coincident with the origin point associated with the mobile device, wherein a view direction of the viewing frustum is aligned with the vector associated with the mobile device; and

determining a plurality of intersections of the plurality of viewing frustums, wherein determining the ROI is based at least on the plurality of intersections of the plurality of viewing frustums.

Aspect 11: The method of aspect 10, wherein determining a plurality of origin points comprises converting a plurality of location data corresponding to the plurality of mobile devices into a plurality of origin points in a mapping coordinate system; and wherein determining a plurality of vectors comprises converting a plurality of orientation data corresponding to the plurality of mobile devices into a plurality of vectors associated with the plurality of origin points, wherein a vector associated with an origin point originates at the origin point.

Aspect 12: The method of aspect 1, further comprising:

receiving the plurality of content streams from the mobile devices;

analyzing the plurality of content streams;

determining at least one object associated with the ROI; and

determining at least one spatial dimension associated with the at least one object;

wherein determining the ROI is based at least on the at least one spatial dimension.

Aspect 13: The method of aspect 1 further comprising:

receiving the plurality of content streams from the mobile devices;

analyzing the plurality of content streams;

determining at least one object associated with the ROI based at least on the plurality of content streams;

determining at least one spatial dimension associated with the at least one object based at least on a predetermined association between a plurality of objects and a plurality of spatial dimensions; and

determining a plurality of distances corresponding to the plurality of mobile devices and the ROI, wherein a distance between a mobile device and the ROI is estimated based on a spatial dimension of the at least one object determined in a content stream captured by the mobile device; wherein determining the ROI is based at least on the plurality of distances.

Aspect 14: The method of aspect 13, wherein determining the plurality of distances comprises determining a plurality of distances corresponding to the plurality of mobile devices, wherein a distance between a mobile device and the ROI is determined based at least on a spatial dimension of the at least one object determined in a content stream captured by the mobile device.

Aspect 15: The method of aspect 1, wherein the sensor data further comprises a distance data representing a distance of the mobile device from at least one object comprised in the ROI, wherein the mobile device comprises a ranging sensor configured to determine the distance.

Aspect 16: The method of aspect 15, wherein the ranging sensor comprises at least one of a LIDAR sensor and an image sensor.

Aspect 17: The method of aspect 1, further comprising:

executing a search on a venue database based on the location data;

determining the venue from a venue database based at least on the location data; and

retrieving at least one spatial characteristic of the venue;

wherein determining the ROI is based at least on the at least one spatial characteristic.

Aspect 18: The method of aspect 1, wherein the plurality of mobile devices comprises a drone configured for flight.

Aspect 19: The method of aspect 15, further comprising determining at least one trajectory associated with at least one mobile device based at least on analysis of the plurality of sensor data; wherein determining the ROI is based at least on the at least one trajectory.

Aspect 20: The method of aspect 1, further comprising: generating a visualization of the ROI and the plurality of mobile devices; and transmitting the visualization to at least one mobile device of the plurality of mobile devices.

Aspect 21: The method of aspect 20, further comprising:

receiving a selection associated with the visualization;

identifying one or more mobile devices associated with the selection; and

transmitting one or more content streams captured by the one or more mobile devices.

Aspect 22: The method of any preceding aspect, wherein the sensor data comprises information indicative of a plurality of viewpoints respective to the ROI, the plurality of viewpoints respective to the ROI is respectively associated with the plurality of content streams.

Aspect 23: The method of any preceding aspect, wherein the device identifier comprises at least one of a device serial number, device identification information, device manufacturer, device type, device model, device component information and a version of software associated with the device.

Aspect 24: The method of any preceding aspect, wherein an attribute associated with the ROI comprises a shape, a size, a spatial dimension, a position, an orientation, and a volume.

Aspect 25: The method of any preceding aspect, wherein determining the ROI comprises determining at least one spatial dimension of the ROI.

Aspect 26: The method of any preceding aspect, wherein the at least one depth is estimated based at least on the origin point closest to the ROI.

Aspect 27: The method of any preceding aspect, further comprising determining a center point of the ROI based at least on the plurality of origin points.

Aspect 28: The method of any preceding aspect, wherein sensor data comprises imagery information.

Aspect 29: The method of any preceding aspect, wherein the plurality of distances is computed based on the center point, wherein a distance of the plurality of distances corresponding to an origin point is between the center point and the origin point.

Aspect 30: The method of any preceding aspect further comprising sampling a plurality of directions associated with the ROI.

Aspect 31: The method of any preceding aspect, wherein a shape of the ROI is estimated based on closest origin point from the center point in each direction of the plurality of directions.

Aspect 32: The method of any preceding aspect, wherein the at least one object is characterized by at least one of a size, a shape, a volume, a position, an orientation and a spatial dimension.

Aspect 33: The method of any preceding aspect, wherein determining the ROI comprises calculating at least one of a two-dimensional volume and a three-dimensional volume.