Multi-target tracking in complex visual environment

In this paper we present a new particle filter based multi-target tracking method incorporating Gaussian Process Dynamical Model (GPDM) to improve robustness in multi-target tracking on complex motion patterns. With the Particle Filter Gaussian Process Dynamical Model (PFGPDM), a high-dimensional training target trajectory dataset of the observation space is projected to a low-dimensional latent space through Probabilistic Principal Component Analysis (PPCA), which will then be used to classify test object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, histogram-Bhartacharyya and GMM Kullback-Leibler are employed respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. Experimental tests are conducted on the PETS2007 benchmark dataset. The test results demonstrate that the approach can track more than four targets with reasonable run-time overhead and good performance.