Tracking and data association
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Research on Dynamic Self-Adaptive Network Security Model Based on Mobile Agent
TOOLS '00 Proceedings of the 36th International Conference on Technology of Object-Oriented Languages and Systems (TOOLS-Asia'00)
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Self-Calibration of a Camera from Video of a Walking Human
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding human interactions with track and body synergies (TBS) captured from multiple views
Computer Vision and Image Understanding
The use of vanishing point for the classification of reflections from foreground mask in videos
IEEE Transactions on Image Processing
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The accuracy of object tracking methodologies can be significantly improved by utilizing knowledge about the monitored scene. Such scene knowledge includes the homography between the camera and ground planes and the occlusion landscape identifying the depth map associated with the static occlusions in the scene. Using the ground plane, a simple method of relating the projected height and width of people objects to image location is used to constrain the dimensions of appearance models. Moreover, trajectory modeling can be greatly improved by performing tracking on the ground-plane tracking using global real-world noise models for the observation and dynamic processes. Finally, the occlusion landscape allows the tracker to predict the complete or partial occlusion of object observations. To facilitate plug and play functionality, this scene knowledge must be automatically learnt. The paper demonstrates how, over a sufficient length of time, observations from the monitored scene itself can be used to parameterize the semantic landscape.