# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 1 Introduction  
Visual object tracking is one of the main tasks in computer vision and has a broad spectrum of applications [1, 2, 3, 4, 5, 6] in precision guidance, automatic driving, computer-aided medicine, and so on. In a nutshell, the task of visual object tracking is to continuously locate the target in a series of video frames, on the premise that we only know the target's bounding box in the initial frame. Actually, due to the limited training data and the inevitable interference of complex scenes, visual object tracking is a complex and challenging problem in reality. In recent years, the object tracking algorithms aimed at short-term tracking have made significant progress in enhancing the tracking performance [7, 8, 9, 10]. Therefore, the Discriminative Correlation Filter (DCF) has drawn significant attention for its computation efficiency [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29], and has shown a state-of-the-art performance on multiple challenging benchmark tracking datasets [30, 31, 32]. The DCF-based trackers can be formulated as a ridged regression model, which learns from a set of object appearance and background training samples obtained by cyclic shifts. They achieve extremely high computing efficiency by utilizing diagonalizing expressions with circulant matrices in the frequency domain. Actually, the video monitoring time is very long, so we expect to track the target correctly over a long time. The DCF-based trackers are widely recognized for their excellent overall performance in short-term tracking. However, they suffer from important issues like target re-detection where once the tracking target is lost, these short-term trackerscannot relocate the target and fail consecutively in subsequent frames due to the no re-detection module. Despite the attempts by various works to solve this issue [33; 34; 35; 36; 37], it is still not completely solved. The reasons include the low adaptability to noticeable appearance changes and low ability to re-detect the target. In this work, we mainly solve the problems of the long-term tracker based DCF in several aspects. First, we try to make the most of the target's static and dynamic information by introducing the motion features to our long-term tracker. Second, we study a low-rank sparse dictionary learning method for re-detection to alleviate the impact of occlusion and noise. Third, we research a new reliability evaluation method to carry out adaptive model updating or decide whether to perform re-detection according to the evaluation results. Massive experiments on OTB-2013 [30], OTB-2015 [30], and Temple Color-128 [15] datasets prove that our proposed method has an obvious improvement compared to the baseline tracker and outperforms several representative state-of-the-art methods in success rate and precision. Besides, our proposed tracking framework can apply to other DCF-based trackers for long-term tracking. In conclusion, the effective contributions in this paper include the following aspects. (1) We introduce motion features as a supplement to the appearance features. By making full use of static and dynamic information, we can improve the adaptability to significant changes in target appearance during long-term tracking and obtain a more robust tracker. The extensive experiments demonstrate that this method can improve the performance of our tracker. (2) We introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. The comparative experiments demonstrate that the tracker's success rate and precision have been dramatically improved by introducing a re-detection module. (3) We propose a new reliability evaluation approach to update the model adaptively by considering both the PSR value and the trajectory smoothness degree. Using this method, our proposed tracker can switch timely between the tracking module and the re-detection module.