Front-view vehicle detection by Markov chain Monte Carlo method
Pattern Recognition
Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Vehicle tracking from disparate views
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Real-time pedestrian and vehicle detection in video using 3D cues
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
View independent recognition of human-vehicle interactions using 3-D models
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Feature-based tracking approach for detection of moving vehicle in traffic videos
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
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Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods will be unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov Chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models.