| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === In recent years, intelligent transportation system is developed to promote the quality of the traffic transportation. In general, concerns of the traffic control center are traffic management, vehicle control, and traffic safety. However, they are not the issues that people concern most. Instead, the situation of traffic congestion is much more useful for the public. In addition, traffic surveillance systems have been widely used for monitoring the roadways. There have been many researches on video analysis of traffic activities such as traffic accidents and violations, but these researches still cannot help people get to know the traffic congestion situation. Therefore, we intend to develop techniques to process traffic surveillance videos for providing people with instant traffic congestion information. In this thesis, a traffic congestion classification framework is proposed for identifying the traffic congestion levels in daytime and nighttime surveillance videos. The degrees of traffic congestion are classified into five levels: jam, heavy, medium, mild and low.
In order to analyze the traffic congestion levels from videos, image processing techniques and the knowledge of classification are indispensable. In the proposed framework, moving vehicles are extracted by background subtraction during the day and by headlight detection at night. Afterward, virtual detectors and virtual detection line are utilized to evaluate and classify the traffic congestion levels in daytime and nighttime surveillance videos, respectively. Moreover, methods of bidirectional roadway detection and lane detection are proposed to extract the consistent features of roadway for the requirements of real-time response and robustness of the frameworks.
In the experiments, we use real freeway surveillance videos captured at day and night to demonstrate the performances on accuracy and computation. Satisfactory experimental results validate the effectiveness of the proposed framework.
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