A Multi-Information Fusion Correlation Filters Tracker

In recent years, trackers based on correlation filters have attracted more and more attention due to the impressive tracking accuracy and real-time performance. However, in real scenarios, the tracking results are often been interfered with by the occlusion, illumination variation, appearance variation and background clutter. In order to find a tracker with better tracking performances, this paper proposed a multi-information fusion correlation filter tracker, which uses channel and spatial reliabilities and time regularization information on samples for filter training, and which not only extends the target search areas but also has a stronger ability to track the targets with significant appearance variations. Thus, results from extensive experiments conducted on OTB100, VOT2016, TC128, and UAV123 data sets show that our tracker with only directional gradient histogram (HOG) and color name (CN) features, performs favorably against the state-of-the-art trackers in terms of tracking precision, tracking success rate, tracking accuracy, and A-R rank.