In recent years, with the continuous development of deep learning technology, deep learning based visual object tracking algorithms have achieved great success. However, in the video, the background, illumination, and the appearance of the target are constantly changing, accompanied by the occurrence of occlusion. This brings great difficulties for visual object tracking. Most of the traditional methods tried to online update the tracker to adapt to the changes in the video. However, the content of the video is complex and changeable, and it is difficult to update and maintain one tracker online to deal with the complex data in the subsequent video, which can easily lead to the accumulation of errors. To solve this problem, based on the existing tracker MDNet, we propose a multi-expert tracker based tracing method. First, the common features of all targets in the video are learned through MDNet, so that the learned features can describe the target better. Then in the tracking process, multiple expert trackers are dynamically constructed according to the tracking results to increase the robustness of the trackers. Finally, the best expert tracker is selected according to the evaluation function of each expert and is used for tracking in the current frame. Experiments show that the proposed method achieves effective tracking results on 25 videos with abrupt changes. Compared with MDNet, the proposed method greatly improves the performance.
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