Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

Exploring probabilistic localized video representation for human action recognition

2011 ◽  
Vol 58 (3) ◽  
pp. 663-685 ◽  
Author(s):  
Yan Song ◽  
Sheng Tang ◽  
Yan-Tao Zheng ◽  
Tat-Seng Chua ◽  
Yongdong Zhang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

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

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Zhong Zhang ◽  
Shuang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Random Projection ◽  
Human Action ◽  
Wireless Sensor ◽  
Real World Applications ◽  
Novel Method

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.

scholarly journals Learning Discriminative Transferable Sparse Coding for Cross-View Action Recognition in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Zhong Zhang ◽  
Shuang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Sparse Representations ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Novel Algorithm

Human action recognition in wireless sensor networks (WSN) is an attractive direction due to its wide applications. However, human actions captured from different sensor nodes in WSN show different views, and the performance of classifier tends to degrade sharply. In this paper, we focus on the issue of cross-view action recognition in WSN and propose a novel algorithm named discriminative transferable sparse coding (DTSC) to overcome the drawback. We learn the sparse representation with an explicit discriminative goal, making the proposed method suitable for recognition. Furthermore, we simultaneously learn the dictionaries from different sensor nodes such that the same actions from different sensor nodes have similar sparse representations. Our method is verified on the IXMAS datasets, and the experimental results demonstrate that our method achieves better results than that of previous methods on cross-view action recognition in WSN.

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