公开(公告)号：US10860859B2

公开(公告)日：2020-12-08

申请号：US16202703

申请日：2018-11-28

Applicant: NVIDIA Corporation

Inventor： Xiaodong Yang ,  Pavlo Molchanov ,  Jan Kautz ,  Behrooz Mahasseni

IPC: G06T7/194 ,  G06K9/00 ,  G06K9/62

Abstract: Detection of activity in video content, and more particularly detecting in video start and end frames inclusive of an activity and a classification for the activity, is fundamental for video analytics including categorizing, searching, indexing, segmentation, and retrieval of videos. Existing activity detection processes rely on a large set of features and classifiers that exhaustively run over every time step of a video at multiple temporal scales, or as a small improvement computationally propose segments of the video on which to perform classification. These existing activity detection processes, however, are computationally expensive, particularly when trying to achieve activity detection accuracy, and moreover are not configurable for any particular time or computation budget. The present disclosure provides a time and/or computation budget-aware method for detecting activity in video that relies on a recurrent neural network implementing a learned policy.

公开(公告)号：US20190163978A1

公开(公告)日：2019-05-30

申请号：US16202703

申请日：2018-11-28

Applicant: NVIDIA Corporation

Inventor： Xiaodong Yang ,  Pavlo Molchanov ,  Jan Kautz ,  Behrooz Mahasseni

IPC: G06K9/00 ,  G06K9/62

Abstract: Detection of activity in video content, and more particularly detecting in video start and end frames inclusive of an activity and a classification for the activity, is fundamental for video analytics including categorizing, searching, indexing, segmentation, and retrieval of videos. Existing activity detection processes rely on a large set of features and classifiers that exhaustively run over every time step of a video at multiple temporal scales, or as a small improvement computationally propose segments of the video on which to perform classification. These existing activity detection processes, however, are computationally expensive, particularly when trying to achieve activity detection accuracy, and moreover are not configurable for any particular time or computation budget. The present disclosure provides a time and/or computation budget-aware method for detecting activity in video that relies on a recurrent neural network implementing a learned policy.