An algorithm for moving vehicle detection and tracking based on adaptive background modeling

作者：You, Feng; Zhang, Ronghui; Wen, Huiying; Xu, Jianmin

来源：Journal of Theoretical and Applied Information Technology, 2012, 45(2): 480-485.

• Adaptive backgrounds
• Background image
• Block noise
• Calculation speed
• Current image
• Detection and tracking
• Difference images
• Dilation operation
• Extended Kalman filtering
• Information contents
• Intelligent transportation systems
• Kalman filter model
• Morphological filtering
• Moving vehicles
• Robust performance
• Traffic environment
• Traffic surveillance
• Vehicle detection

In modern traffic surveillance, computer vision methods have commonly been utiliezed to detect vehicles because of the rich information content contained in an image. And detection and tracking of moving vehicle in traffic environment is one of the most important components in intelligent transportation system (ITS). The adaptive background modeling method was used to eliminate the negative effects from moving vehicle and rebuild the background images. The moving vehicles were segmented with difference images between background and current images. To suppress noise caused by segmentation and improve robust performance of vehicle detection, a template with 3-by-3 window was utilized to decrease isolated noise points around vehicle contours. Then, the morphological filtering, including erosion and dilation operation, was also applied, which minimizes the influence of the discontinuous block noise. Finally, to reduce the searching scope of vehicle detected and improve the calculation speed, Kalman filter model was performed to track motive vehicles. The experimental results verifid the effectiveness and real-time of algorithm.

• 出版日期2012-11
• 单位华南理工大学