Hidden Markov model-unscented Kalman filter contour tracking: A multi-cue and multi-resolution approach

This paper present a novel attempt to introduce an HMM-based multi-resolution and multi-cue segmentation in combination with the unscented Kalman filter tracking method. It combines multiple features distribution and multiple resolutions to facilitate 2D video tracking. The advantages of this method lie in its speed and its robustness. Speed is dramatically improved by taking into account multiple resolutions which reduce number of measurement points (number of HMM states) while keeping its quality. Robustness is achieved by using multiple cues. We propose an algorithm to find an optimal operating point for a tracker in terms of the image scale. Furthermore, we propose a faster multi-scale (spatial) tracker based on a minimum acceptable performance limit. The proposed method is demonstrated on human head tracking with a non-stationary camera. Visual tests indicate that the optimized algorithms produce qualitatively better results. Results show that we are able to maintain real-time processing on quite generous video resolutions. Therefore it will be shown that our approach is faster and more efficient than conventional UKF and UKF with multi-cue.