Adaptive multi-resolution CRF-based contour tracking
This paper presents a novel tracker, based on combining a linear chain conditional random field (CRF) adaptive multi-resolution segmentation with an unscented Kalman filter (UKF). Specifically, the proposed method combines multiple features and multiple resolutions to facilitate video tracking. The advantages of our method lie in its speed and robustness. Speed is dramatically improved by taking into account multiple resolutions in one dimensional CRF-based segmentation. Robustness is achieved by using multiple cues. The performance of the proposed method is demonstrated in human head tracking with a non-stationary camera. Results show that we are able to maintain real-time processing on quite generous video sequences. The paper argues that our approach is faster, more efficient and more robust than the conventional UKF.