IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organized Integration of Adaptive Visual Cues for Face Tracking
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Object Reacquisition Using Invariant Appearance Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
Multi-modal tracking using texture changes
Image and Vision Computing
Real-Time Tracking Based on Particle Filter and Multi-cue
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 02
A motion observable representation using color correlogram and its applications to tracking
Computer Vision and Image Understanding
CamShift guided particle filter for visual tracking
Pattern Recognition Letters
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variance reduction techniques in particle-based visual contour tracking
Pattern Recognition
3D human motion tracking based on a progressive particle filter
Pattern Recognition
Dynamic multi-cue tracking with detection responses association
Proceedings of the international conference on Multimedia
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
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This paper made major research on the target representation problem, which plays a significant role in visual tracking, but has received little attention in most researches. In order to fulfill the requirements of tracking robustness and effectiveness in practical conditions, a dynamic appearance model is constructed. Due to particle filter's excellent characteristics, it is employed in this paper not only to estimate target's state, but also to construct the dynamic observation model integrated by multiple cues. In the proposed method, a dynamic multi-cue integration model is constructed for particle filter framework. And a systematic study is done on evaluating cue's weight. Specially, a particle filter based weight tracker is designed to update multi-cue's integrating manner online, so as to adapt the observation model to target's appearance changes. In such a way, a double-particle-filter based tracking framework is formed, and it is field tested on a variety of videos in different tracking conditions. In the experiments and comparisons, the applicable conditions of the proposed dynamic model are discussed, and its robustness and effectiveness are demonstrated.