Robust tracking with motion estimation and kernel-based color modelling

Visual tracking is still a challenging problem in computer vision. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. In this work, we propose a new method to track arbitrary objects using both sum-of-squared differences (SSD) and color-based mean-shift (MS) trackers in the Kalman filter framework. The SSD and the MS trackers complement each other by overcoming their respective disadvantages. The rapid model change in SSD tracker is overcome by the MS tracker module, while the inability of MS tracker to handle large displacements and occlusions is circumvented by the SSD module. In addition, rapid scale changes of the object generated by camera ego-motion or zooming are measured by a global affine motion estimation. Finally, the global appearance model on which MS relies is updated, based on the Bhattacharyya distance between this target model and current candidate model. This permits to tackle global appearance changes of the object. The performance of the proposed tracker is better than the individual SSD and MS trackers.