Video-based traffic data collection system for multiple vehicle types

Abstract

Traffic data of multiple vehicle types are important for pavement design, traffic operations and traffic control. A new video-based traffic data collection system for multiple vehicle types is developed. By tracking and classifying every passing vehicle under mixed traffic conditions, the type and speed of every passing vehicle are recognised. Finally, the flows and mean speeds of multiple vehicle types are output. A colour image-based adaptive background subtraction is proposed to obtain more accurate vehicle objects, and a series of processes like shadow removal and setting road detection region are used to improve the system robustness. In order to improve the accuracy of vehicle counting, the cross-lane vehicles are detected and repeated counting for one vehicle is avoided. In order to reduce the classification errors, the space ratio of the blob and data fusion are used to reduce the classification errors caused by vehicle occlusions. This system was tested under four different weather conditions. The accuracy of vehicle counting was 97.4% and the error of vehicle classification was 8.3%. The correlation coefficient of speeds detected by this system and radar gun was 0.898 and the mean absolute error of speed detection by this system was only 2.3 km/h. © The Institution of Engineering and Technology 2014.