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Local Random Sparse Coding for Human Action Recognition in Wireless Sensor Networks

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  • Zhong Zhang
  • Shuang Liu

Abstract

Recognizing human action in wireless sensor networks (WSN) has raised a great interest owing to the requirements of real-world applications. Recently, the bag-of-features model (BOF) has proved effective in human action recognition. In this paper, we propose a novel method named local random sparse coding (LRSC) for human action recognition in WSN based on the BOF model. The contribution is twofold. First, we utilize random projection (RP) technique for each feature vector to alleviate the curse of dimensionality. Second, we consider the locality of codebook and correspondingly propose to reconstruct the features using similar codewords. Our method is verified on the KTH and UCF Sports databases, and the experimental results demonstrate that our method achieves better results than that of previous methods on human action recognition in WSN.

Suggested Citation

  • Zhong Zhang & Shuang Liu, 2015. "Local Random Sparse Coding for Human Action Recognition in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 11(10), pages 726369-7263, October.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:10:p:726369
    DOI: 10.1155/2015/726369
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