CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Statistical color models with application to skin detection
International Journal of Computer Vision
Video-Based Face Recognition Evaluation in the CHIL Project - Run 1
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Foreground regions extraction and characterization towards real-time object tracking
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Objective Evaluation of Pedestrian and Vehicle Tracking on the CLEAR Surveillance Dataset
Multimodal Technologies for Perception of Humans
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This paper presents the tracking system from Athens Information Technology that participated to the pedestrian and vehicle surveillance task of the CLEAR 2006 evaluations. Two are the novelties of the proposed tracker. First, we use a variation of Stauffer's adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman filter in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman filter. In the feedback path, the Kalman filter adapts the learning parameters of the adaptive background module. Second, we combine a temporal persistence pixel map, together with edge information, to produce the evidence that is associated with targets. The proposed tracker performed well in the evaluations, and can be also applied to indoors settings and multi-camera tracking.