We formulate human motion analysis as a high dimensional optimization problem.A generative method is proposed in the framework of evolutionary computation.Firstly, we learn the nonlinear latent space of motion state and use a manifold reconstruction method to establish the inverse mapping.In the search strategy, we apply clonal selection algorithm (CSA) for human pose optimization in the learnt latent space, which proves to be more efficient and accurate.Then, we introduce the temporal information into the traditional CSA and propose a sequential CSA (S-CSA) method for motion tracking.Experimental results show that our method achieves better results than state-of-art methods.
李毅, 吴振锋, 孙亭. 子空间中的克隆选择人体运动分析方法[J]. 电子学报, 2015, 43(6): 1101-1107.
LI Yi, WU Zhen-feng, SUN Ting. Human Motion Analysis in Latent Space Using Clonal Selection Algorithm. Chinese Journal of Electronics, 2015, 43(6): 1101-1107.
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2015, Vol. 43 
