Multi-aspect Target Tracking in Image Sequences Using Particle Filters

Abstract

AbstractThis 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 multi-aspect target tracking in terms of robustness, accuracy and complexity.KeywordsProbability Density FunctionParticle FilterObservation ModelTracking ProblemTarget TemplateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.