CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Kalman Filtering and Mean Shift for Real Time Eye Tracking under Active IR Illumination
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head Detection and Tracking by Mean-Shift and Kalman Filter
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
Differential Earth Mover's Distance with Its Applications to Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective appearance model and similarity measure for particle filtering and visual tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A new approach for adaptive background object tracking based on Kalman filter and mean shift
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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A method for object tracking combining the accuracy of mean shift with the robustness to occlusion of Kalman filtering is proposed At first, an estimation of the object's position is obtained by the mean shift tracking algorithm and it is treated as the observation for a Kalman filter Moreover, we propose a dynamic scheme for the Kalman filter as the elements of its state matrix are updated on-line depending on a measure evaluating the quality of the observation According to this measure, if the target is not occluded the observation contributes to the update equations of the Kalman filter state matrix Otherwise, the observation is not taken into consideration Experimental results show significant improvement with respect to the standard mean shift method both in terms of accuracy and execution time.