| 英文摘要 | Since the 21st century, the pressure of traffic system on urban roads is increasing, with the accelerating development of China’s urbanization. More and more traffic congestion, accidents, etc., occur nowadays, which gradually become the global common concern in the economic and social development. Automatic Incident Detection System (AIDS) is one of the chief solutions to this problem. In this paper, the method for detecting traf- fic congestion—one of common traffic abnormal events—is studied on the basis of video images taken by a dynamic PTZ camera. Our research work consists of three parts, which are the PTZ camera monitoring scenarios mosaiking, moving objects detection, and traffic congestion detection and analysis based on traffic information parameters. In the first part, we use SURF feature-matching to estimate transformation model of multi-frames and mosaic a panorama image for the PTZ camera monitoring scenarios. In order to achieve a fast and precise mosaiking effect, we introduce Levenberg-Marquard (LM) algorithm for parameter optimization. In the second part, a method of “Rapid Detection of Pano-ramic Im- ages” is proposed, which is based on the thought of coarse-to-fine posi- tioning. Firstly, we get the corresponding region by histogram algorithm;then find the exact position by feature-matching method; thirdly, separate the foreground and background based on background subtraction; finally,extract the accurate objects through morphological processing. The algorithm achieves real-time and accurate detection effect. In the third part, traffic congestion detection and analysis, we propose a novel method about extracting traffic information parameters based on moving detection. A redefinition of traffic congestion is proposed, and the traffic status is classified into three categories: smooth, slight congestion, and serious congestion. The algorithm firstly extracts the road as a region of interest (ROI), and then calculates the two parameters that queue length and space occupancy through detecting road vehicles, at last reports the traffic status based on these parameters. Experiment results indicate that this method can detect traffic congestion successfully． |
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