# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 2 Related Work
### Long-Term Trackers  
Different from short-term trackers, long-term trackers need to address the problems of the re-detection of the target after tracking failure and the low adaptability to significant appearance changes. Many algorithms are proposed to acquire impressive performance in long-term tracking.  
Recently, most algorithms decompose the tracking task into multiple modules to handle long-term tracking. For example, in [42], [PERSON] et al. design a framework (TLD) that breaks the tracking task down into multiple subtasks--tracking, learning and detection. The tracker and detector can promote each other. This framework performs favourably against these existing trackers in long-term tracking. Following the idea of TLD, [PERSON] et al. [33] teach a DCF-based tracker to estimate the translation and scale variation, and train a complementary detector by utilizing online random ferns to relocate the target. In [34], LT-FLO proposes a two-module approach--the short-term module and the long-term module. The short-term module adopts edge-based features to enhance the ability of the tracker to light changes. The long-term module combines the online model of appearance and the learned pose manifold to re-detect the target. In [35], MUSTer propose a dual-component approach that combines the long-term and short-term memory. MUSTer employs an integrated correlation filter for short-term memory and keypoint matching based RANSAC for long-term memory. In [36], [PERSON] et al. integrate a color-based model into the discriminative correlation filter and adopt a simple sparse coding based method to acquire reliable searching areas[PERSON] Since most of the aforementioned re-detection modules adopt intensive sampling, the target template and background template may contain many similar components, which may affect the performance of the long-term tracker. In this work, we investigate the re-detection module to improve the ability to distinguish background and target[PERSON]  
In addition, in long-term tracking, the visual feature representation has a great influence on tracker results[PERSON] A majority of the DCF-based trackers only use the appearance features extracted from a single frame, such as HOG [43], color histogram [15], CN [17], convolutional neural network (CNN) features [16; 18; 19]. For example, [PERSON] et al. [44] adopt the HOG features extracted from background-aware samples to train multi-channel correlation filters. [PERSON] et al. [45] use the CNN features to provide better feature representation for the discriminative correlation filter. However, because the appearance target may change significantly during tracking, the target appearance model is not always good at distinguishing the target from the background. In [46; 47; 48], some studies have shown that motion features reflect the information between frames and are complementary to the appearance features. Hence, we investigate the combination of appearance features and motion features during tracking.  
Besides, it is essential to identify the state of the object in long-term tracking. In [11], [PERSON] et al. adopt the peak-versus-noise ratio (PSR) based on the correlation response map to update the tracking model adaptively. Moreover, LMCF [49] updates the tracker by the Average Peak to Correlation (APCE) criterion, reflecting the fluctuation of the response map and the confidence level of the tracking result. However, when the background is cluttered, the adoption of PSR or APCE for reliability estimation may lead to the tracker's wrong prediction. Therefore, in this work, we investigate a better estimate of the reliability of modeling an adaptive update and timely switching between the tracking module and the re-detection module.