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

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.