The Research on Vehicle Flow Detection in Complex Scenes
- 1 December 2008
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 154-158
- https://doi.org/10.1109/isise.2008.207
Abstract
Vehicle flow detection plays an important role in ITS. In the process of vehicle flow detection, the vehicle is contiguous with another and the same vehicle counted repeatedly are common problems, especially the problem of changing lanes, which is very difficult to solve. This paper uses the method that combines background difference and virtual-loop sensor to detect vehicle flow, which based on the fast adaptive background updating method, and it introduces a new method of multi-lane sharing one detecting region. Experiment results show that this method can update the background exactly along with the variance of illumination; it also can solve vehicle orientation and vehicle changing lane problems. In real traffic scenes, it can detect vehicle flow accurately and meet the real-time processing demand.Keywords
This publication has 9 references indexed in Scilit:
- Background subtraction based on cooccurrence of image variationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Adaptive background mixture models for real-time trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Efficient region-based motion segmentation for a video monitoring systemPattern Recognition Letters, 2003
- A new approach to morphological color image processingPattern Recognition, 2002
- State-of-the-art of vehicular traffic flow modellingProceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2001
- Image Processing Techniques For Real-Time Qualitative Road Traffic Data AnalysisReal-Time Imaging, 1999
- Metastable states in cellular automata for traffic flowZeitschrift für Physik B Condensed Matter, 1998
- Image Difference Threshold Strategies and Shadow Detection.Published by British Machine Vision Association and Society for Pattern Recognition ,1995
- Minimum error thresholdingPattern Recognition, 1986