  Existing visual tracking methods usually localize a target object with a
bounding box, in which the performance of the foreground object trackers or
detectors is often affected by the inclusion of background clutter. To handle
this problem, we learn a patch-based graph representation for visual tracking.
The tracked object is modeled by with a graph by taking a set of
non-overlapping image patches as nodes, in which the weight of each node
indicates how likely it belongs to the foreground and edges are weighted for
indicating the appearance compatibility of two neighboring nodes. This graph is
dynamically learned and applied in object tracking and model updating. During
the tracking process, the proposed algorithm performs three main steps in each
frame. First, the graph is initialized by assigning binary weights of some
image patches to indicate the object and background patches according to the
predicted bounding box. Second, the graph is optimized to refine the patch
weights by using a novel alternating direction method of multipliers. Third,
the object feature representation is updated by imposing the weights of patches
on the extracted image features. The object location is predicted by maximizing
the classification score in the structured support vector machine. Extensive
experiments show that the proposed tracking algorithm performs well against the
state-of-the-art methods on large-scale benchmark datasets.
