Abstract
Visual tracking is one of the most important components in numerous applications of computer vision. Although correlation filter based trackers gained popularity due to their efficiency, there is a need to improve the overall tracking capability. In this paper, a tracking algorithm based on the kernelized correlation filter (KCF) is proposed. First, fused features including HOG, color-naming, and HSV are employed to boost the tracking performance. Second, to tackle the fixed template size, a scale adaptive scheme is proposed which strengthens the tracking precision. Third, an adaptive learning rate and an occlusion detection mechanism are presented to update the target appearance model in presence of occlusion problem. Extensive evaluation on the OTB-2013 dataset demonstrates that the proposed tracker outperforms the state-of-the-art trackers significantly. The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original KCF tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes.
Highlights
Visual tracking is a challenging topic in the computer vision for its various applications in video surveillance, automatic driving, and medical fields
The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original kernelized correlation filter (KCF) tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes
From the success plots of one pass evaluation (OPE), we can see that our tracker achieves the best performance with average overlap threshold 0.590 which gets a 14.79% improvement upon KCF (0.514)
Summary
Visual tracking is a challenging topic in the computer vision for its various applications in video surveillance, automatic driving, and medical fields. Generative tracking [2, 3] focuses on learning a target appearance model and it locates the target by searching the region that is most similar to the appearance model It does not require a large dataset for training. Great progress has been made in the two categories of tracking algorithms, it remains a challenging task to generalize the target appearance model from a limited set of training samples. Based on CSK, Henriques et al [9] introduced the kernelized correlation filter (KCF) into the tracking application and adopted the HOG feature instead of raw pixel to improve both the accuracy and robustness of the tracker. We proposed an adaptive learning rate to track the target and give an occlusion detection mechanism.
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