Abstract
Of different learning-based methods in human tracking, many state-of-the-art approaches have been dedicated to reduce the dimensionality of the pose state space in order to avoid complex searching in a high dimensional state space. Seldom research on human tracking refers shared latent model. In this paper, We propose a method of shared latent dynamical model (SLDM) for human tracking from monocular images. The shared latent variables can be determined easily if state vectors and observation vectors are statistically independent.With a SLDM prior over state space and observation space, our approach can be integrated into a Bayesian tracking framework of Condensation, and further a scheme of variance feedback is designed to avoid mis-tracking. Experiments using simulations and real images demonstrate this human tracking method is very efficient and promising.
Keywords
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Tong, M., Liu, Y. (2007). Shared Latent Dynamical Model for Human Tracking from Videos. In: Sebe, N., Liu, Y., Zhuang, Y., Huang, T.S. (eds) Multimedia Content Analysis and Mining. MCAM 2007. Lecture Notes in Computer Science, vol 4577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73417-8_17
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DOI: https://doi.org/10.1007/978-3-540-73417-8_17
Publisher Name: Springer, Berlin, Heidelberg
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