2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-33.5
Paper Title HUMAN-AWARE COARSE-TO-FINE ONLINE ACTION DETECTION
Authors Zichen Yang, Di Huang, Beihang University, China; Jie Qin, Inception Institute of Artificial Intelligence, United Arab Emirates; Yunhong Wang, Beihang University, China
SessionIVMSP-33: Action Recognition
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this work, we propose a two-stage framework to efficiently and effectively detect actions on-the-fly. An action location network (ALN) is developed in the first stage to judge whether the current frame is action-related, while the second stage involves an action classification network (ACN) to further identify the action category. In this way, irrelevant negative frames are quickly discarded and actions are detected as early as they occur. Moreover, we highlight human areas at both the stages by respectively incorporating a human detector and a human mask layer. As a result, more accurate spatial-temporal windows of actions are detected, based on which more robust features are extracted for classification. Experimental results on two popular benchmarks demonstrate the superior performance of the proposed approach.