Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
Computer Vision and Image Understanding
| Hi-index | 0.00 |
We present an efficient and robust measurement model for visual tracking. This approach builds on and extends work on measurement model of subspace representation. Subspace-based tracking algorithms have been introduced to visual tracking literature for a decade and show considerable tracking performance due to its robustness in matching. However, the measures used in their measurement models are not robust enough in cluttered backgrounds. We propose a novel measure of object matching referred to as WDIFS, which aims to improve the discriminability of matching within the subspace. Our measurement model can distinguish target from similar background clutters which often cause erroneous drift by conventional DFFS based measure. Experiments demonstrate the effectiveness of the proposed tracking algorithm under cluttered background.