Tracking deformable target via multi-cues active contours

In this study, we present a novel multi-cues active contours based method for tracking target contours using edge, region, and shape information. To locate the target position, a contour based meanshift tracker is designed which combines both color and texture information. In order to reduce the adverse impact of sophisticated background and accelerate the curve motion, we extract rough target region from the coming frame by the proposed target appearance model. What’s more, both discriminative pre-learning based global layer and voting based local layer are integrated into our appearance model. For obtaining the detailed target boundaries, we embed edge, region, and shape information into the level sets based multi-cues active contour model (MCAC). Experiments on seven video sequences demonstrate that the proposed method performs better than other competitive contour tracking methods under various tracking environment.