Object tracking is one of the most important problems in computer vision. The aim of video tracking is toextract the trajectories of a target or object of interest, i.e. accurately locate a moving target in a videosequence and discriminate target from non-targets in the feature space of the sequence. So, featuredescriptors can have significant effects on such discrimination. In this paper, we use the basic idea of manytrackers which consists of three main components of the reference model, i.e., object modeling, objectdetection and localization, and model updating. However, there are major improvements in our system.Our forth component, occlusion handling, utilizes the r-spatiogram to detect the best target candidate.While spatiogram contains some moments upon the coordinates of the pixels, r-spatiogram computesregion-based compactness on the distribution of the given feature in the image that captures richer featuresto represent the objects. The proposed research develops an efficient and robust way to keep tracking theobject throughout video sequences in the presence of significant appearance variations and severeocclusions. The proposed method is evaluated on the Princeton RGBD tracking dataset consideringsequences with different challenges and the obtained results demonstrate the effectiveness of the proposedmethod.
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