A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Multi-mode Target Tracking on a Crowd Scene
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
The Research on Vehicle Flow Detection in Complex Scenes
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 01
A Video-Based Real-Time Vehicle Counting System Using Adaptive Background Method
SITIS '08 Proceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
Motion Detection Based on Background Modeling and Performance Analysis for Outdoor Surveillance
ICCMS '09 Proceedings of the 2009 International Conference on Computer Modeling and Simulation
Image matching using distance transform
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Nonlinear image labeling for multivalued segmentation
IEEE Transactions on Image Processing
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In general, the vision-based methods may face the problems of serious illumination variation, shadows, or swaying trees. Here, we propose a novel vehicle detection method without background modeling to overcome the aforementioned problems. First, a modified block-based frame differential method is established to quickly detect the moving targets without the influences of rapid illumination variations. Second, the precise targets' regions are extracted with the dual foregrounds fusion method. Third, a texture-based object segmentation method is proposed to segment each vehicle from the merged foreground image blob and remove the shadows. Fourth, a false foreground filtering method is developed based on the concept of motion entropy to remove the false object regions caused by the swaying trees or moving clouds. Finally, the texturebased target tracking method is proposed to track each detected target and then apply the virtual-loop detector to compute the traffic flow. Experimental results show that our proposed system can work with the computing rate above 20 fps and the average accuracy of vehicle counting can approach 86%.