Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.