Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Roadside camera calibration and its application in length-based vehicle classification
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops
Journal of Visual Communication and Image Representation
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As traffic surveillance technologies continue to grow worldwide, vehicle detection, counting and tracking are becoming increasing important. This paper proposes a real-time multi-vehicle detection and tracking approach. Lane marker detection is carried out for vehicle counting on each lane. It also helps remove the foreground noise and shadow. Instead of tracking the entire vehicle blob, vehicle sub-feature based Kalman filter is used in tracking. By implementing sub-feature tracking, this system is more robust to partial occlusions, which happens a lot in congestions. This approach is scalable to most freeway surveillance video. Several freeway surveillance videos are used to evaluate the performance of the traffic surveillance system. The proposed approach is compared with the standard blob tracking. Test results demonstrated that our approach outperforms blob tracking in correct tracking rate. The performance of our method is also illustrated with different video image sampling rates.