Mean Shift and Optimal Prediction for Efficient Object Tracking

A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the target candidate that is the most similar to a given target model, while the prediction of the next target location is computed with a Kalman filter. The dissimilarity between the target model and the target candidates is expressed by a metric based on the Bhattacharyya coefficient. The implementation of the new method achieves real-time performance, being appropriate for a large variety of objects with different color patterns. The resulting tracking, tested on various sequences, is robust to partial occlusion, significant clutter, target scale variations, rotations in depth, and changes in camera position.