Online tracking of deformable objects under occlusion using dominant points

Abstract

This paper deals with tracking of deformable objects in the presence of occlusion using dominant point representation of the boundary contour. A novel nonintegral time propagation model for propagating the dominant points is proposed. It uses an initial guess generated from a linear operation and an analytical conjugate gradient approach for online robust learning of the shape deformation and motion model. A scheme is presented to automatically detect and correct the region of large local deformation. In order to deal with occlusion, admissible restrictions on deformation and motion of the object are automatically determined. The proposed method overcomes the need of offline learning and learns the deformation and motion model of the object using very few initial frames of the input video. The performance of the method is demonstrated using varieties of videos of different objects. © 2013 Optical Society of America.