Object tracking via dual fuzzy low-rank approximation

Abstract

For the object loss problem in the tracking process caused by illumination, occlusion, pose variation, and motion blur, the tracking method based on dual fuzzy low-rank approximation in a particle filter framework is proposed in this paper. Firstly, multiple constraint regions are built to filter insignificant samples, and more distinguished candidate samples are selected. Secondly, dual fuzzy observation function of each candidate sample is created based on the designed low-rank approximation representations of object and background. Then the generalized tracking results are obtained by computing membership degrees of dual fuzzy observation functions. Finally, based on the spatial coherency principle, the final tracking result is determined from the generalized results by measuring similarities of consecutive objects. The proposed method shows good performance as compared with several state-of-the-art trackers on challenging benchmark sequences.