- Research Article
- Open Access
Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
EURASIP Journal on Image and Video Processing volume 2008, Article number: 969456 (2008)
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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Wang, J., Yin, Y. & Man, H. Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model. J Image Video Proc 2008, 969456 (2008) doi:10.1155/2008/969456
- Particle Filter
- Motion State
- Appearance Model
- Observation Space
- Target Trajectory