M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
Robust People Tracking with Global Trajectory Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
KNIGHT/spl trade/: a real time surveillance system for multiple and non-overlapping cameras
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
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This paper presents an approach to real-time 3D object tracking in cluttered scenes using multiple synchronized and calibrated cameras. The goal is to accurately track targets over a long period of time in the presence of complete occlusion in some of the camera views. In the proposed system, color histogram was used to represent object appearance. Tracked 3D object locations were smoothed and new locations predicted using a Kalman filter. The predicted object 3D location was then projected onto all camera views to provide a search region for robust 2D object tracking and occlusion detection. The experimental results were validated using ground-truth data obtained from a marker-based motion capture system. The results illustrate that the proposed approach is capable of effective and robust 3D tracking of multiple objects in cluttered scenes.