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Over these years, correlation filters based trackers have shown edges both in accuracy and speed. However, variations of target appearance caused by heavy occlusion, rotation, background clutters and target deformations are the major challenges for tracking. To solve these problems, many works put on exploiting the power of target representation, such as high-level convolutional features. Nonetheless, these methods make a great compromise between the speed and performance. At the same time, there are few researches on improving the performance of model updater and the ensemble methods. In this paper, a multi-experts joint decision strategy base on kernelized correlation filters is proposed to obtain robust and accurate visual tracking, two trackers with handcrafted features and deep convolutional neural network features are integrated in this framework. We also investigate the mechanism of tracking failure caused by occlusion and background clutters, and propose a novel criterion to evaluate the reliability of samples. Our work includes extending the kernelized correlation filter-based tracker with the capability of handling scale changes as well. The proposed tracker is extensively evaluated on the OTB-2013, OTB-2015 and VOT2015 benchmark datasets. Compared with the state-of-the-art trackers, the distinguished experimental results demonstrate the effectiveness of the proposed framework.

Multi-experts Joint Decision with Adaptive Model Updater for Robust Visual Tracking