Amazon Technologies, Inc.

Stephen Gould

Andrea Olgiati

Joseph Patrick Tighe

Davide Modolo

Stefano Stefani

Gaurav D Ghare

Hao Chen

Roman Goldenberg

Ranju Das

Vasant Manohar

Jonathan Hedley

Vivek Bhadauria

Vuong Van Le

Nataliya Shapovalova

Nitin Singhal

Gowtham Jeyabalan

Praveen Kumar Udayakumar

Enrica Maria Filippi

Kuang Han

Renjun Zheng

Camille Claire Le Men

People can be tracked across multiple segments of video data, which can correspond to different scenes in a single video file, or multiple video streams or feeds. An instance of video data can be broken up into segments that can each be analyzed to determine faces and bodies represented therein. The bodies can be analyzed across frames of the segment to determine body tracklets that are consistent across the segment. Associations of faces and bodies can be determined based using relative distances and/or spatial relationships. A subsequent clustering of these associations is performed to attempt to determine consistent associations that correspond to unique individuals. Unique identifiers are determined for each person represented in one or more segments of an instance of video data. Such an approach enables individual representations to be correlated across multiple instances.

Person tracking across video instances

A data processing pipeline controller receives a request, from a data iterator associated with a machine learning model, for a data output of a module in the data processing pipeline, wherein each module in the data processing pipeline has an associated cache. The controller determines whether a data output of the module is stored in the associated cache and responsive to the data output being stored in the associated cache, provides the data output from the associated cache to the data iterator without processing data through the module.

Data iterator with automatic caching

Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Analysis of video content

A method and system for processing multiple frames of a video by a neural network are provided. Two frames of a video may be analyzed to determine if at least a portion of the layer-by-layer processing by a neural network can be skipped or terminated. Processing of a first frame of the video is performed by the neural network. A next frame of the video is processed by the neural network, such that processing of fewer layers (or sets of operations) of the neural network is performed if the first frame and the second frame are substantially similar.

Deep learning processing of video

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Seattle, WA, United States of America

Stephen Gould