Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions Using Level Sets

Using a camera, the visual object tracking is one of the most important process in searching the spot of moving object over the time. In the case of the object moves fast relative to the frame rate, the visual object tracking is difficult task. The active contour evolution algorithm which is used for the tracking of object in a given frame of an image sequence. Active contour based visual object tracking using the level sets is proposed which does not consider the camera either stationary or moving. We present a framework for active contour-based visual object tracking using the level sets. The main components of our framework consist of the contour-based tracking initialization, color-based contour evolution, the adaptive shape-based contour evolution for the non-periodic motions, the dynamic shape-based contour evolution for the periodic motions and handling of the abrupt motions. For the contour-based tracking initialization, we use an optical flow-based algorithm for the automatically initializing contours at the first frame. In the color-based contour evolution, we use Markov random field theory to measure correlations between values of the neighboring pixels for the posterior probability estimation. In the adaptive shape-based contour evolution, we combined the global shape information and the local color information to hierarchically develop gradually the contour, and a flexible shape updating model is made. In the dynamic shape based contour evolution, a shape mode transition matrix is gain to characterize the temporal correlations of the object shapes. In the handling of abrupt motions, particle swarm optimization (PSO) is used to capture the global motion which are applied to this contour in the current frame to produce an initial contour in the next frame.