Real-Time Multi-Person Action Recognition with a Neural Compute Stick
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발행년도
2021
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저널명
21st International Conference on Control, Automation and Systems (ICCAS)
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저자
Young-Chul Yoon , Hyeonseok Jung
초록
Deep learning has successfully boosted a performance of action recognition and inspired model developments for it. Specifically, 3D convolutional neural network (CNN) which best fits the purpose of vision-based action recognition has been applied to the task in various forms. In this paper, comprehensive research process for practical multi-person action recognition is presented. We perform various experiments using 3D CNN considering both performance and time efficiency. Distinguished from previous studies, we consider a performance on an embedded platform which consists of an embedded computer, ZED2 camera and a neural compute stick. The neural compute stick has its own memory and can be utilized asynchronously. This is a pioneer work proposing a multi-person action recognition framework using a neural compute stick. Step-by-step experiments verify a validity of the model configuration and the proposed framework.
연관키워드
Deep learning, Three-dimensional displays, Automation, Computational medeling, Control system, Cameras, Real-time system