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Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model


We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a high-dimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, Histogram-Bhattacharyya, GMM Kullback-Leibler, and the rotation invariant appearance models are employed, respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. The simulation results demonstrate that the approach can track more than four targets with reasonable runtime overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusion.

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Correspondence to Jing Wang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wang, J., Yin, Y. & Man, H. Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model. J Image Video Proc 2008, 969456 (2008).

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