Abstract
With the rapid development of surveillance technology, there are often several cameras in one scenario. The multi-camera usage to perform gait recognition becomes a challenge problem. This paper studies multi-camera gait recognition via structure sparsity. For the multi-camera structure in the training set, we propose a structure sparsity algorithm to learn informative and discriminative sparse representations; and for the structure in the testing set, we develop a new classification criteria based on the reconstruction error of learned sparse representations. In addition, we learn a dictionary from the original gait data to further improve recognition accuracy meanwhile reduce computational cost. Experimental results show that the proposed method can efficiently deal with the multi-camera gait recognition problem and outperforms the state-of-the-art sparse representation methods.
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Yin, Q., Sun, R., Wang, L., He, R. (2012). Structure Sparsity for Multi-camera Gait Recognition. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_33
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DOI: https://doi.org/10.1007/978-3-642-33506-8_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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