Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions MECS1302

Abstract: 

              In this paper, we present a framework for active contour-based visual tracking using level sets. The maincomponents of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shapebased contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contourbased tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For thecolor-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour,and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame.