Correlation-Filter Based Scale-Adaptive Visual Tracking With Hybrid-Scheme Sample Learning

In visual tracking, a mature scale estimation method can greatly improve tracking performance and provide accurate target information for model training. However, many visual tracking approaches ignore the scale estimation problem or adopt a heuristic and exhaustive scale-estimation strategy. In this paper, we propose a novel correlation-filter based visual tracking approach that reveals the missing link between scale estimation and the detection response. In contrast to many multi-scale visual trackers, which generate samples at different scales using some pre-designed criteria and then select the sample with the maximal classifier response, in this paper, we deduce a scale estimation equation based on detection responses; thus, the scale of the target object can be estimated mathematically. To obtain a more stable estimated object scale, a constraint function that considers the prior knowledge of visual tracking is proposed. Moreover, a hybrid sample learning scheme is formulated to select pertinent training samples with higher learning weights to train the appearance model. Our tracker operates under a framework of correlation filters to achieve a high tracking speed. We demonstrate the efficiency and robustness of our proposed tracking algorithm by comparing it with 14 other state-of-the-art trackers on all the video sequences in the object tracking benchmark (OTB) 2013 dataset.