Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM Computing Surveys (CSUR)
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Analytics in Urban Environments
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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Virtual boundary (tripwire) crossing detection is an essential component in almost all modern digital visual surveillance systems. In this paper, we address the problem of achieving reliable tripwire crossing results in crowded scenarios and propose a new technique for the replacement of conventional tracking based tripwire techniques. We introduce the concept of "ground patches" which are a set of sub-regions sampled around the defined tripwire. Each ground patch is declared active when it is occupied by one or more foreground objects. Temporal and appearance features are extracted from each of the patches, and they are correlated across the virtual boundary to determine if a crossing occurs. The proposed method has been applied in situations with different traffic loads, and promising results are obtained.