# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 1 Introduction  
Object tracking is an important issue for video analysis in the field of computer vision, with wide-ranging applications including surveillance, human-computer interaction and medical imaging. Given the first frame including the target location and the size of the bounding box, the object tracking task is to estimate the location of the target in the current frame without the target motion information in advance [1]. In recent years, along with many new tracking algorithms emerging, the performance of visual tracking has been greatly promoted. However, object tracking is still a challenging issue due to many problems existing in the tracking process such as illumination variation, occlusion, large deformations and background clutters, which are not solved well. Therefore, object tracking still needs to be investigated. In this paper, we tackle the complex background challenges in the bounding box caused by target occlusion, large deformations or background clutters in visual tracking. As an important branch in visual tracking, many tracking-by-detection algorithms [2; 3; 4; 5; 6; 7; 8; 9] have attracted much attention. The idea of tracking-by-detection algorithms is regarded as finding target localization as a classification problem. The classifier is trained with positive and negative samples corresponding to foreground and background areas, respectively. During the tracking process, the object always has occlusion, deformation or size variation, leading to the bounding box containing too much background area, which brings a negative influence to the classifier updating. To reduce the effect of an enlarged background, a new representation for the target was proposed, called the spatially-ordered and weighted patch descriptor (SOWP) [10], which conveys structural informationof the object within the bounding box. However, SOWP uses a calculation method similar to Euclidean distance to calculate the similarity between two patches, as well as many current tracking algorithms [6; 11; 12; 13], which makes it difficult to distinguish the target from the background when there are some appearance similarities brought by background blur, occlusion or fast motion in the bounding box. In order to improve the above problem and considering that the minimum barrier distance (MBD) [14] is more robust to pixel value fluctuation caused by motion or noise, we construct a robust object representation by applying MBD to calculate the weights of patches. Given the bounding box of a target in the search area, we divide it into non-overlapping patches and describe them with a combination of an RGB (Red-Green-Blue) color histogram and a histogram of oriented gradients (HOG) feature. Then, we expand the bounding box outwards, and the boundary patches in the extended bounding box are regarded as the seed patches according to the appearance-based backgroundness cue [15; 16]. The image boundary prior assumes that most of the image boundary area is background [15], so we set the boundary patches as the background seed set. Instead of generating an affinity matrix by calculating the similarity between patches, we construct the MBD-based descriptor through a distance transform map for visual tracking. Specifically speaking, we use each patch as a node and two adjacent nodes to form an edge and obtain a distance transform map by iteratively calculating the MBD from each patch to the seed set. The weight of each node represented by the value of the MBD describes how likely this patch belongs to the target. The larger the value of the MBD, the smaller the similarity of the current patch to the boundary patch and the greater the probability that the current patch belongs to the target. As shown in Figure 1, the target in the sequence \"Skating2-1\" is a female skater, and it is obvious that the video includes fast motion and deformation due to the rapid movement of the skater.