IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Dynamic Bayesian Network Approach to Multi-cue based Visual Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
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It is still a challenging problem for tracking objects in complex visual situations, such as an object is occluded or the object's color features are very similar to its background. Therefore, a novel visual tracking algorithm is proposed for multiple cues fusion bused on three common cues: color, target position prediction and motion continuity in this paper. Color feature is free of translation and rotation and robust to partial occlusions and pose variations. Features of target position prediction and motion continuity can handle the condition that the color difference between the foreground and the background is similar. Combining with CAMSHIFT (Continuously Adaptive Mean Shift) technique, experimental results show that the proposed visual tracking algorithm is more robust than traditional single cue and gets better trucking effect than CMST (Collaborative Mean Shift Tracking). Successful rates of the proposed algorithm are 70% to 100% in 4 different complex conditions.