Abstract: Generally discussed herein are devices, systems, and methods for privacy-preserving video. A method can include identifying which classes of objects are present in video data, for each class of the classes identified in the video data, generating respective video streams that include objects of the class and exclude objects not of the class, and providing each of the respective video streams to a content distribution network.

Abstract: Generally discussed herein are devices, systems, and methods for privacy-preserving video. A method can include identifying which classes of objects are present in video data, for each class of the classes identified in the video data, generating respective video streams that include objects of the class and exclude objects not of the class, and providing each of the respective video streams to a content distribution network.

Abstract: Methods and devices for encoding and decoding data streams are disclosed. In some aspects, the data streams are multimedia data streams. One method disclosed includes obtaining, by a client device, a first multimedia data stream and a second multimedia data stream, the second multimedia data stream being a lower fidelity version of the first multimedia data stream, generating, by the client device, a third multimedia data stream based on differences between the first and second multimedia data streams, compressing, by the client device, the second multimedia data stream to generate a first compressed multimedia data stream, compressing, by the client device, the third multimedia data stream to generate a second compressed multimedia data stream; and transmitting, by the client device, the first and second compressed multimedia data steams to the server.

Abstract: A thin-cloud system for distributing content, for example, live streaming video content, from a broadcaster to a viewer is provided herein. The computing devices of the broadcaster can provide the multi-bitrate transcoding, of the two or more bitstreams, sent to a file server, which alleviates the need for the file server to encode the streams for a viewer. These multiple streams are received by a file server for provision to one or more viewers. The viewers can receive the streams at one of the two or more bitrates. If the viewer receives the content at a lower bitrate, the viewers can employ a machine learning (ML) co-processor that can operate as an accelerator to improve the inbound content, if that content is provided at a lower bitrate, and thus, a lower resolution. The file server can train and provide the ML models used for the acceleration.

Abstract: Techniques for ability enhancement are described. In some embodiments, devices and systems located in a transportation network share threat information with one another, in order to enhance a user's ability to operate or function in a transportation-related context. In one embodiment, a process in a vehicle receives threat information from a remote device, the threat information based on information about objects or conditions proximate to the remote device. The process then determines that the threat information is relevant to the safe operation of the vehicle. Then, the process modifies operation of the vehicle based on the threat information, such as by presenting a message to the operator of the vehicle and/or controlling the vehicle itself.

Abstract: This document relates to performing live video stream analytics on edge devices. One example determines resources available to the system, and a video analytics configuration is selected that distributes work between edge devices and cloud devices in a cascading manner, where edge device processing is prioritized over cloud processing in order to conserve resources. This example can dynamically modify the allocation of processing depending on changing conditions, such as network availability.

Abstract: A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.

Abstract: A system and method are disclosed for providing a real-time wireless video surveillance system. The video surveillance system leverages edge computing to enable wireless video surveillance distributing video processing between edges of the network and the cloud to reduce the amount of video that is uploaded to the cloud for analysis.

Abstract: A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: A system and method are disclosed for providing a real-time wireless video surveillance system. The video surveillance system leverages edge computing to enable wireless video surveillance distributing video processing between edges of the network and the cloud to reduce the amount of video that is uploaded to the cloud for analysis.

Abstract: Methods and devices for encoding and decoding data streams are disclosed. In some aspects, the data streams are multimedia data streams. One method disclosed includes obtaining, by a client device, a first multimedia data stream and a second multimedia data stream, the second multimedia data stream being a lower fidelity version of the first multimedia data stream, generating, by the client device, a third multimedia data stream based on differences between the first and second multimedia data streams, compressing, by the client device, the second multimedia data stream to generate a first compressed multimedia data stream, compressing, by the client device, the third multimedia data stream to generate a second compressed multimedia data stream; and transmitting, by the client device, the first and second compressed multimedia data steams to the server.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: A method can include classifying, using a compressed and specialized convolutional neural network (CNN), an object of a video frame into classes, clustering the object based on a distance of a feature vector of the object to a feature vector of a centroid object of the cluster, storing top-k classes, a centroid identification, and a cluster identification, in response to receiving a query for objects of class X from a specific video stream, retrieving image data for each centroid of each cluster that includes the class X as one of the top-k classes, classifying, using a ground truth CNN (GT-CNN), the retrieved image data for each centroid, and for each centroid determined to be classified as a member of the class X providing image data for each object in each cluster associated with the centroid.

Abstract: Described is a technology by which ambient data related to a vehicle is sensed and processed, for use in determining a state change related to external traffic awareness. Based upon the state change, an allowed level of interactivity with a user interface may be changed, and/or a notification may be output. Images and/or depth data may be sensed as part of determining whether a user who is interacting with a device in a stopped vehicle is to be made aware of the changed condition with respect to other vehicles, pedestrians and/or the like.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: An example method for managing a wireless network can include receiving, via a processor, an access point (AP) beam shape and an AP transmit direction corresponding to each of a plurality of access points (APs). The example method can also include receiving, via a processor, a client device beam shape profile from at least one of the plurality of access points. The example method can also further include calculating, via the processor, a location of a blocking object based on the AP beam shape, the AP transmit direction, and the client device beam shape profile from the plurality of access points. The example method can further include allocating, via the processor, client-to-AP associations based at least in part on the location of the blocking object.

Abstract: An environment is described in which a processing system provides application-level usage information to users. In one scenario, for example, the processing system may provide personal usage information to a user who is operating a user device. The personal usage information itemizes the amount of data (and/or other resources) that has been consumed by each application run by the user device. In another scenario, the processing system may provide expected usage information associated with at least one candidate application provided by a marketplace system. The expected usage information describes an expected consumption of data (and/or other resources) by the candidate application upon running the candidate application by the user device. The processing system can tailor the expected usage information that it sends to a particular user based on user profile data. The user profile data describes a manner in which users operate applications.

Abstract: A global manager communicates with various local managers to receive and process video queries. The video queries identify components that process live video streams, placement options for where the components of the video query may be executed, and various video query plans. The video query plans include options such as framerate and video quality. As the global manager processes the video queries, the global manager determines an initial set of video query configurations that identify a video query plan and placement option for each component of a given video query. Using the initial set of video query configurations, the global manager then determines an optimal set of video query configurations for the received set of video queries. The global manager communications instructions to the local managers to execute the components of the video queries using the video query plans and placement options from the optimal set of video query configurations.

Abstract: Various technologies described herein pertain to performing video analytics. The approaches set forth herein support live video analytics at scale with approximate and delay-tolerant processing. Video streams can be captured by multiple cameras and continuously streamed to a video analytics computing system; the video streams can be received at the video analytics computing system. Multiple video analytics queries can be executed on the video streams. The multiple video analytics queries can be concurrently executed by the video analytics computing system on the video streams as the video streams are continuously streamed to the video analytics computing system. The multiple video analytics queries can be executed utilizing resources of the video analytics computing system allocated between the multiple video analytics queries. Execution of the multiple video analytics queries can return respective results for the multiple video analytics queries.