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Object tracking is one of the most important problems in computer vision. Theaim of video tracking is to extract the trajectories of a target or object ofinterest, i.e. accurately locate a moving target in a video sequence anddiscriminate target from non-targets in the feature space of the sequence. So,feature descriptors can have significant effects on such discrimination. Inthis paper, we use the basic idea of many trackers which consists of three maincomponents of the reference model, i.e., object modeling, object detection andlocalization, and model updating. However, there are major improvements in oursystem. Our forth component, occlusion handling, utilizes the r-spatiogram todetect the best target candidate. While spatiogram contains some moments uponthe coordinates of the pixels, r-spatiogram computes region-based compactnesson the distribution of the given feature in the image that captures richerfeatures to represent the objects. The proposed research develops an efficientand robust way to keep tracking the object throughout video sequences in thepresence of significant appearance variations and severe occlusions. Theproposed method is evaluated on the Princeton RGBD tracking dataset consideringsequences with different challenges and the obtained results demonstrate theeffectiveness of the proposed method.

Applying r-spatiogram in object tracking for occlusion handling