Real-Time Traffic Flow Analysis without Background Modeling

• Main Authors: Ming-Hsiu Tsai, 蔡明修
• Other Authors: Cheng-Chang Lien
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/76484125438905239886

碩士 === 中華大學 === 資訊工程學系(所) === 98 === In this study, the research goal is to analyze the traffic flow automatically with the vision-based method. However, the vision-based methods may face the problems of serious illumination variation, moving shadows and influences of moving clouds 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 changes and camera shaking problems. Second, the precise targets’ regions are extracted by 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 moving 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 texture-based target tracking method is proposed to track each detected target and then apply the virtual-loop detector to analyze the traffic flow. Furthermore, the traffic jam event is also detected in the proposed system. Experimental results show that our proposed system can work in real time with the computing rate above 20 fps and the average of accuracy of vehicle counting can approach 86%.