Efficient action recognition from compressed depth maps
Jie Miao, Xiaoyi Jia, Reji Mathew, Xiangmin Xu, David Taubman, Chunmei Qing · 2016
We propose an efficient action recognition scheme based solely on compressed depth maps. Each depth map is coded by a recently proposed scalable encoder that employs multi-scale breakpoints and an adaptive discrete wavelet transform (DWT). DWT coefficients describe smooth variations in depth while breakpoints communicate sharp boundaries. Both of these attributes are extracted from the bit-stream and utilized to construct features which are subject to a classification scheme for human action recognition. By extracting features from the compressed bit-stream computational complexity is significantly reduced thereby making the proposed scheme suitable for real-time applications. A L2-regularized collaborative representation classifier is employed for classification. The proposed scheme is computationally more efficient when compared with conventional approaches. Experimental results on the MSR 3D action dataset validate the effectiveness and efficiency of our proposed scheme.