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- Title
Local Random Sparse Coding for Human Action Recognition in Wireless Sensor Networks.
- Authors
Zhang, Zhong; Liu, Shuang
- 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.
- Subjects
CODING theory; HUMAN behavior; PATTERN recognition systems; WIRELESS sensor networks; RANDOM projection method; DATABASES
- Publication
International Journal of Distributed Sensor Networks, 2015, Vol 2015, p1
- ISSN
1550-1329
- Publication type
Article
- DOI
10.1155/2015/726369