Vehicle Detection Based On Road Detection

Intelligent Transportation System (ITS) is the development trend of transportation system. Advanced information technology, data communication Technology, electronic sensing technology, control technology and computer technology are integrated in ITS, which constitute the comprehensive, effective, accurate and real-time transportation system. Advanced traffic monitoring and computer processing technology is an important area in ITS.The videos captured by a static camera are studied in this paper, and a vehicle detection method based on road detection is proposed. The road detection which is typically used in in-car system, is used to determine the interest areas of vehicle detection. It efficiently reduces the complexity of the vehicle detection.The paper first gives a summary of motion detection methods and background modeling methods, analyzes and compares them with each other, and chooses the background subtraction method to detect the vehicles, uses Time Median Background Model Method to form a background image, then gives a background update method.In the second part, it uses filters to reduce the noises, according to the features of the background image. The summary and comparison of image pre-processing algorithm is discussed in this paper, and the appropriate edge detection method is chosen, a road detection algorithm based on gray value chosen is raised, the traditional Hough Transform is improved.According to the information of roads, it determines the interest areas of vehicle detection, uses a threshold operation to get the moving object, and apply Clustering algorithm to form the MERs(Minimum Enclosing Rectangle), and then takes count of MERs to finish the vehicle counted.At the end, a road video which continues 2minute and 47second , is used to test the system improved by this paper. Test Results show our system can efficiently detect the road-line in clear day, and get a high rate of recognition. However, it still requires more work on how to compute traffic parameters in the future, like speed, vehicle length, etc. It also need more research on how to apply this system to the unstructured road which have a more flexible environment.