Improved particle filter gaussian process dynamical model for multitarget tracking

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, appearance models are employed 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 run-time overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusions.