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Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition

Ke, Q., Liu, J., Bennamoun, M., Rahmani, H., An, S., Sohel, F. and Boussaid, F. (2019) Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition. In: Jawahar, C., Li, H., Mori, G. and Schindler, K., (eds.) Computer Vision – ACCV 2018. Springer, pp. 729-745.

Link to Published Version: https://doi.org/10.1007/978-3-030-20870-7_45
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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.

Item Type: Book Chapter
Murdoch Affiliation: Information Technology, Mathematics and Statistics
Publisher: Springer
Copyright: © 2019 Springer Nature Switzerland AG
Other Information: Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)
URI: http://researchrepository.murdoch.edu.au/id/eprint/46358
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