Adaptive weighted real-time compressive tracking

Many tracking methods often suffer from the drift problems caused by appearance change. Therefore developing a robust online tracker is still a challenging test. Recently, a simple yet effective and efficient tracking algorithm has been proposed by compressive tracking (CT) paradigm to alleviate the drift to some degree. The CT tracker introduced an appearance model based on features extracted from the multi-scale image feature space in the compressed domain. However, the CT tracker may detect the positive sample that is less important because it does not discriminatively consider the sample importance in its learning procedure. In this study, the authors integrate the sample importance into the CT tracker online learning procedure. They also add an efficient feature select method which can choose the most discriminative power weak classifier and employ the co-training criterion into CT tracker to improve the tracking performance. Experiments show that the proposed tracker demonstrates the superior performance in robustness and efficiency than other state-of-the-art trackers.