Regressed Importance Sampling on Manifolds for Efficient Object Tracking

Authors:
Fatih Porikli;Pan Pan
Affiliations:
-;-
Venue:
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Year:
2009

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Abstract

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 demonstrate the improved pose (affine) tracking performance of the proposed method compared with the original regression tracker and particle filters.