  Object tracking is one of the most important problems in computer vision. The
aim of video tracking is to extract the trajectories of a target or object of
interest, i.e. accurately locate a moving target in a video sequence and
discriminate target from non-targets in the feature space of the sequence. So,
feature descriptors can have significant effects on such discrimination. In
this paper, we use the basic idea of many trackers which consists of three main
components of the reference model, i.e., object modeling, object detection 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 computes region-based compactness
on the distribution of the given feature in the image that captures richer
features to represent the objects. The proposed research develops an efficient
and robust way to keep tracking the object throughout video sequences in the
presence of significant appearance variations and severe occlusions. The
proposed method is evaluated on the Princeton RGBD tracking dataset considering
sequences with different challenges and the obtained results demonstrate the
effectiveness of the proposed method.
