In this paper, we present a novel generative method for human motion tracking. The principle contribution is the development of clonal selection algorithm for pose analysis in latent space of human motion. Firstly, we use ISOMAP to learn the low-dimensional latent space of pose state and a manifold reconstruction method is proposed to establish the smooth mappings between the latent and original space. Pose analysis is performed in this latent space, which results to be more efficient and accurate. Secondly, we apply a new evolutionary approach, clonal selection algorithm (CSA) for pose optimization. Then, we design a CSA based method for pose estimation, which can achieve viewpoint invariant 3D pose reconstruction from static images. Thirdly, in order to make CSA suitable for motion tracking, we propose a sequential CSA (S-CSA) framework by incorporating the temporal continuity information into the traditional CSA. Our methods are demonstrated in different motion types and different image sequences. Experimental results show that our method achieves better results than state-of-art methods.
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