Algorithmic Fusion for More Robust Feature Tracking
International Journal of Computer Vision
Moving object tracking under varying illumination conditions
Pattern Recognition Letters
A particle filter for joint detection and tracking of color objects
Image and Vision Computing
Pattern Analysis & Applications
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Real-time video-shot detection for scene surveillance applications
IEEE Transactions on Image Processing
A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people
International Journal of Approximate Reasoning
A DSmT-Based approach for data association in the context of multiple target tracking
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
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In this paper, we propose an efficient and robust method for multiple targets tracking in cluttered scenes using multiple cues. Our approach combines the use of Monte Carlo sequential filtering for tracking and Dezert-Smarandache theory (DSmT) to integrate the information provided by the different cues. The use of DSmT provides the necessary framework to quantify and overcome the conflict that might appear between the cues due to the occlusion. Our tracking approach is tested with color and location cues on a cluttered scene where multiple targets are involved in partial or total occlusion.