In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios.
Based on the sharing features of a variety of license plates (LP), the vertical edge was first detected by Sobel edge detector. Then, some approaches were adopted to remove the invalid edge regarding the characteristics of edge grayscale jump and edge density, so that the regions having features of LP were preserved. Next, by horizontal and vertical projections and mathematical morphology (MM) operation, the LP region was searched. Then, color-reversing judgement was conducted by color analysis, and binarization was done based on core region in LP. Afterward, characters were segmented by means of prior knowledge and connected components analysis, and character recognition was conducted based on radial basis function (RBF) neural network. With abundant samples verified in dark hours and daytime under real conditions, the experiment indicates that it is feasible to adopt this algorithm in license plate recognition system (LPRS) to achieve accuracy.
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