Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Global Regularizer and Temporal-aware Cross-entropy for Skeleton-based Early Action Recognition
AU - Ke, Qiuhong
AU - Liu, Jun
AU - Bennamoun, Mohammed
AU - Rahmani, Hossein
AU - An, Senjian
AU - Sohel, Ferdous
AU - Boussaid, Farid
PY - 2018/12/2
Y1 - 2018/12/2
N2 - 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.
AB - 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.
M3 - Conference contribution/Paper
SN - 9783030208691
VL - IV
BT - Computer Vision - ACCV 2018
PB - Springer
T2 - 14th Asian Conference on Computer Vision (ACCV)
Y2 - 4 December 2018
ER -