Regressed Importance Sampling on Manifolds for Efficient Object Tracking

In this paper, a new integrated particle filter is proposed for video object tracking. After particles are generated by importance sampling, each particle is regressed on the transformation space where the mapping function is learned offline by regression on pose manifold using Lie algebra, leading to a more effective allocation of particles. Experimental results on synthetic and real sequences clearly demonstrated the improved pose (affine) tracking performance of the proposed method compared with the original regression tracker and particle filters. AVSS 2009 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contri...

• Type: Conference
• Year: 2009
• Where: AVSS
• Authors: Fatih Porikli, Pan Pan
• Keywords: AVSS 2009, Electric Research Laboratories, Mitsubishi Electric Research, Particle Filters, Signal Processing