Illumination Invariant Object Tracking with Incremental Subspace Learning

In this paper, we present an efficient and robust subspace learning based object tracking algorithm with special illumination handling. Illumination variances pose a great challenge to most of object tracking algorithms. In this paper, an edge orientation based feature has been proposed and proven to approximately invariant to illumination changes. Besides, we utilize the incremental subspace learning based particle filter framework which is effective to handle various appearance changes. To reduce the amount of computation when the particle number is large, a new layer of preprocessing step has been added to the particle filter framework with the help of edge orientation features. From the experimental m results, it is obvious that our proposed algorithm achieves promising performance especially in the scenarios with large illumination changes.