11 October 2013 Position and locality constrained soft coding for human action recognition
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Although the traditional bag-of-words model has shown promising results for human action recognition, in the feature coding phase, the ambiguous features from different body parts are still difficult to distinguish. Furthermore, it also suffers from serious representation error. We propose an innovative coding strategy called position and locality constrained soft coding (PLSC) to overcome these limitations. PLSC uses the feature position in a human oriented region of interest (ROI) to distinguish the ambiguous features. We first construct a subdictionary for each feature by selecting the bases from their spatial neighbor in human ROI. Then, a modified soft coding with locality constraint is adopted to alleviate the quantization error and preserve the manifold structure of features. This novel coding algorithm increases both the representation accuracy and discriminative power with low computational cost. The human action recognition experimental results on KTH, Weizmann, and UCF sports datasets show that PLSC can achieve a better performance than previous competing feature coding methods.
© 2013 SPIE and IS&T
Bin Wang, Bin Wang, Yu Liu, Yu Liu, Wenhua Xiao, Wenhua Xiao, Wei Xu, Wei Xu, Maojun Zhang, Maojun Zhang, } "Position and locality constrained soft coding for human action recognition," Journal of Electronic Imaging 22(4), 041118 (11 October 2013). https://doi.org/10.1117/1.JEI.22.4.041118 . Submission:


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