Object Tracking Scheme Using Part-Based Correlation Filters

Object tracking remains a challenging problem in computer vision. Recently, methods based on correlation filters have achieved good performance in benchmarks. Usually, most of these visual trackers rely on tracking the object as a whole, not being able to handle the object variations. This paper proposes a scheme using several local correlation filters combined with a global correlation filter for improving the performance of object tracking methods based on correlation filters. We integrated this scheme into the traditional Kernelized Correlation Filter (KCF) method to evaluate our proposed approach. Experiments show that the proposed scheme is consistent and achieves better results compared to the baseline.