Bilayer model for cross-view human action recognition based on transfer learning

Yandi Li; Xiping Xu; Jiahong Xu; Enyu Du

doi:10.1117/1.JEI.28.3.033016

25 May 2019 Bilayer model for cross-view human action recognition based on transfer learning

Yandi Li, Xiping Xu, Jiahong Xu, Enyu Du

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033016 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033016

Abstract

In cross-view action recognition, there remains a challenge that the action representation will lack the ability of transfer learning when the feature space changes. To solve this problem, a cross-view action recognition approach using a bilayer discriminative model is proposed. We first extract the key poses to capture the essence of each action sequence and represent each key pose by a bag of visual words (BoVW) in a single view. We then construct a bipartite graph between the heterogeneous poses and apply multipartitioning to cocluster the view-dependent visual words for developing the cross view bags of visual words feature, which is more discriminative in the presence of view changes. The novelty is to design a bilayer classifier consisting of SVM and HMM at the frame level and sequence level, respectively, to make up for the loss of temporal information when using a BoVW to represent the whole action sequence. Finally, DTW is used as a pruning algorithm to lessen the number of nodes for searching the Viterbi path. Extensive experiments are performed on two well-known multiple view action datasets IXMAS and N-UCLA, and a detailed performance comparison with the existing view-invariant action recognition techniques indicates that the proposed method works equally well in accuracy and efficiency.

Citation Download Citation

Yandi Li, Xiping Xu, Jiahong Xu, and Enyu Du "Bilayer model for cross-view human action recognition based on transfer learning," Journal of Electronic Imaging 28(3), 033016 (25 May 2019). https://doi.org/10.1117/1.JEI.28.3.033016

Received: 20 November 2018; Accepted: 25 April 2019; Published: 25 May 2019

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