A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Frame-Rate Omnidirectional Surveillance & Tracking of Camouflaged and Occluded Targets
VS '99 Proceedings of the Second IEEE Workshop on Visual Surveillance
Reliable background suppression for complex scenes
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking video objects in cluttered background
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper a robust segmentation process for detecting incidents on highways is presented. This segmentation process is based on background subtraction and uses an efficient background model initialisation and update to work 24/7. A cross-correlation based shadow detection is also used for minimising ghosts. It is also proposed a stopped vehicle detection system based on the pixel history cache. This methodology has proved to be quite robust in terms of different weather conditions, lighting and image quality. Some experiments carried out on some highway scenarios demonstrate the robustness of the proposed solution.