Robust Object Tracking via Combining Observation Models

Fan JIANG  Guijin WANG  Chang LIU  Xinggang LIN  Weiguo WU  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D    No.3    pp.662-665
Publication Date: 2010/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.662
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
object tracking,  combine observation models,  feature fusion,  particle filter,  

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Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.