Neural-edge-based vehicle detection and traffic parameter extraction

摘要

Vehicle detection is a fundamental component of image-based traffic monitoring system. In this paper, we propose a neural-edge-based vehicle detection method to improve the accuracy of vehicle detection and classification. In this method, the feature information is extracted by the seed-filling-based method and is presented to the input of neural network for vehicle detection and classification. The neural-edge-based vehicle detection method is effective and the correct rate of vehicle detection is higher than 96%, independent of environmental conditions. Also, traffic parameters, such as vehicle count, vehicle class, and vehicle speed, are extracted via vehicle tracking method