Abstract
In this paper, we present a novel generative method for articulated human motion tracking. The principle contribution is the development of a modified Particle Swarm Optimization (PSO) algorithm for pose optimization in latent space of human motion. There are three characteristics in the proposed method. Firstly, we learn the latent space of human motion using PCA and perform human motion analysis in this latent space, which results to be more efficient and accurate. Secondly, we introduce simulated annealing into traditional PSO. A new algorithm, termed annealed PSO (APSO) is designed for pose optimization, which can get global optimum solution more efficiently. Lastly, we apply APSO for human pose estimation. And a sequential APSO (SAPSO) method is proposed for motion tracking. Experimental results on different motion types and different image sequences show that our method achieves better results than state-of-art methods.
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Li, Y., Sun, Z. (2012). Articulated Human Motion Tracking by Sequential Annealed Particle Swarm Optimization. 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_20
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DOI: https://doi.org/10.1007/978-3-642-33506-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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