Skeleton based action recognition using pose change map and convolutional neural networks

Boxiang Hou Sr.; Guohui Tian Sr.; Bin Huang Sr.

doi:10.1117/12.2539634

14 August 2019 Skeleton based action recognition using pose change map and convolutional neural networks

Boxiang Hou Sr., Guohui Tian Sr., Bin Huang Sr.

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111791L (2019) https://doi.org/10.1117/12.2539634
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Recent skeleton-based action recognition approaches have achieved significant improvement by using convolutional neural networks. These methods usually map skeleton sequences into images, and an end-to-end CNN is adopted for label prediction. In this paper, a novel image mapping method is proposed, named pose change map (PCM), which provides a visual indication of how the similarity between human pose and atoms of pose dictionary changes over time. Then, PCM as well as raw skeleton coordinates are fed into CNN to extract robust and discriminative features for action recognition. Experiments on two challenging datasets NTU RGB+D and UTKinect-Action consistently demonstrate the superiority of our method.

Citation Download Citation

Boxiang Hou Sr., Guohui Tian Sr., and Bin Huang Sr. "Skeleton based action recognition using pose change map and convolutional neural networks", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791L (14 August 2019); https://doi.org/10.1117/12.2539634

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