# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 2 Related Work  
Visual tracking is an important issue in computer vision, as it is the foundation of a high level visual task. The current tracking algorithms can be divided into two major categories: generative models [22; 23; 24; 25] and discriminative models [26; 27; 28; 29; 30; 31]. By describing the target appearances using generative models such as subspace models, generative methods search for regions most similar to the target object and updating the appearance model dynamically to solve object tracking. In general, the generative models focus on the characterization of the target and ignore the background information; hence, they are prone to drift when the target changes dramatically and often fail in a cluttered background [21]. In contrast, discriminative trackers build a binary classifier, which focuses on differentiating the target from the background [32]. Belonging to discriminative trackers, adaptive tracking-by-detection approaches build a classifier during tracking and update the classifier using generated binary labeled training samples around the current object location [21]. To avoid the label errors during the classification step, the kernelized structured output support vector machine (SVM) has been used in [33] for adaptive visual object tracking. However, this method uses simple low-level features and has low efficiency at handling occlusion. Therefore, generating correct labeled training samples is very important for the classifier updating, and designing a good sample descriptor is crucial for distinguishing positive or negative samples clearly. As we mentioned above, tracking-by-detection approaches almost perform not well when there is complex background information in the bounding box, which inevitably exists, caused by occlusion, deformation, size variation and background clutters. Many works have been proposed to improve the robustness of trackers to handle the influence of these challenging problems. [PERSON] et al. [34] proposed a pixel-wise appearance model called pixel-wise spatial pyramid, which employs the pixel feature vector to combine several features. This pixel-wise spatial pyramid contributes to the accurate location of the tracked object, especially for drastic appearance change. [PERSON] et al. [35] proposed a locality sensitive histogram, taking into account the contributions from each pixel and facilitating robust multi-region tracking. However, these pixel-based trackers failed to handle occlusion and cluttered background due to pixel-wise appearance models lacking structural information. For improving some pixel-based trackers, superpixel-based trackers [36; 37; 38] then came out to handle non-rigid and deformable targets, by segmenting images into superpixels and providing the semantic structure of the target. [PERSON] and [PERSON] et al. [36; 39] built the superpixel-based appearance models, with some effects on object occlusion and deformation. However, trackers based on superpixels are complex and have difficulty dealing with cluttered backgrounds, which often lead to unstable results. In recent years, patch-based tracking methods have received more attention, because patch-based trackers can describe the structure information of the object better, and they often use different descriptors to represent the object [10; 12; 40]. As a new and effective method, SOWP [10] decomposed the object bounding box into multiple patches, and the patch weights were calculated by performing random walk with restart simulations. The weighted patch descriptor [10] is more flexible for partial occlusion and appearance changes of the object. Similar to SOWP, some methods [41; 42] weight the sample with patch likelihoods in particle filters, and [43] used adaptive weighting in patch-based correlation filters to mitigate the effect of occlusion. However, when the object is occluded for a long time, SOWP still has drift, and it is difficult to distinguish target and background when there are some appearance similarities brought by background blur or target occlusion in the bounding box.