CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Design and implementation of an optical flow-based autonomous video surveillance system
EuroIMSA '08 Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications
A survey on vision-based human action recognition
Image and Vision Computing
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Visual tracking by adaptive kalman filtering and mean shift
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
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Mean shift algorithm is one of popular methods to visual object tracking and has some advantages comparing to other tracking methods. Aiming at the shortcoming of the Mean shift algorithm, this paper proposed a novel object tracking approach using Kalman filter and adaptive background Mean shift. On the one hand, the combination of Kalman filter with Mean shift is suit to handle the case of target appearance drastically changing and occlusion. On the other hand, Bayes law is used to adjust the color probability distribution. It enables objects to be tracked, even when move across regions of background which are the same color as a significant portion of the object. Experimental results demonstrate that this algorithm can track the object accurately in conditions of abrupt shifts, as well as clutter and partial occlusions occurring to the tracking object with good robustness.