CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dynamics Based Robust Motion Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sputnik Tracker: Having a Companion Improves Robustness of the Tracker
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Dynamic subspace-based coordinated multicamera tracking
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.00 |
Tracking objects in the presence of clutter and occlusion remains a challenging problem. Current approaches often rely on a priori target dynamics and/or use nearly rigid image context to determine the target position. In this paper, a novel algorithm is proposed to estimate the location of a target while it is hidden due to occlusion. The main idea behind the algorithm is to use contextual dynamical cues from multiple supporter features which may move with the target, move independently of the target, or remain stationary. These dynamical cues are learned directly from the data without making prior assumptions about the motions of the target and/or the support features. As illustrated through several experiments, the proposed algorithm outperforms state of the art approaches under long occlusions and severe camera motion.