Multi-Object Tracking Through Clutter Using Graph Cuts

The standard graph cut technique is a robust method for globally optimal image segmentations. However, because of its global nature, it is prone to capture outlying areas similar to the object of interest. This paper proposes a novel method to constrain the standard graph cut technique for tracking anywhere from one to several objects in regions of interest. For each object, we introduce a pixel penalty based upon distance from a region of interest and so segmentation is biased to remain in this area. Also, we employ a filter predicting the location of the object. The distance penalty is then centered at this location and adoptively scaled based on prediction confidence. This method is capable of tracking multiple interacting objects of different intensity profiles in both gray-scale and color imagery.