Particle Filter for Targets Tracking with Motion Model

Real-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.published_or_final_versio

Multi-aspect Target Tracking in Image Sequences Using Particle Filters

Abstract. This paper addresses the issue of multi-aspect target tracking where target’s aspect is modeled by a continuous-valued affine model. The affine parameters are assumed to follow first-order Markov models and augmented with target’s kinematic parameters in the state vector. Three particle filtering algorithms, Sequential Importance Re-sampling (SIR), the Auxiliary Particle Filter (APF1), and a modified APF (APF2) are implemented and compared along with a new initialization technique. Specifically, APF2 involves two likelihood functions and a re-weighting scheme to balance the diversity and the focus of particles. Simulation results on simulated infrared image sequences show the proposed APF2 algorithm significantly outperforms SIR and APF1 algorithms for multiaspect target tracking in terms of robustness, accuracy and complexity.