Book chapter
Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition
Computer Vision – ACCV 2018, Vol.11364, pp.729-745
Springer
2019
Abstract
In this paper, we propose a new approach to recognize the class label of an action before this action is fully performed based on skeleton sequences. Compared to action recognition which uses fully observed action sequences, early action recognition with partial sequences is much more challenging mainly due to: (1) the global information of a long-term action is not available in the partial sequence, and (2) the partial sequences at different observation ratios of an action contain a number of sub-actions with diverse motion information. To address the first challenge, we introduce a global regularizer to learn a hidden feature space, where the statistical properties of the partial sequences are similar to those of the full sequences. We introduce a temporal-aware cross-entropy to address the second challenge and achieve better prediction performance. We evaluate the proposed method on three challenging skeleton datasets. Experimental results show the superiority of the proposed method for skeleton-based early action recognition.
Details
- Title
- Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition
- Authors/Creators
- Q. Ke (Author/Creator)J. Liu (Author/Creator)M. Bennamoun (Author/Creator)H. Rahmani (Author/Creator)S. An (Author/Creator)F. Sohel (Author/Creator)F. Boussaid (Author/Creator)
- Contributors
- C. Jawahar (Editor)H. Li (Editor)G. Mori (Editor)K. Schindler (Editor)
- Publication Details
- Computer Vision – ACCV 2018, Vol.11364, pp.729-745
- Publisher
- Springer
- Identifiers
- 991005541789307891
- Copyright
- © 2019 Springer Nature Switzerland AG
- Murdoch Affiliation
- Information Technology, Mathematics and Statistics
- Language
- English
- Resource Type
- Book chapter
- Additional Information
- Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)
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