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.
Video-based traffic data collection system for multiple vehicle types
IET Intelligent Transport Systems ; 8 , 2 ; 164-174
2014-03-01
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
speed detection , vehicle type , passing vehicle classification , colour image-based adaptive background subtraction , image fusion , passing vehicle speed , pavement design , shadow removal process , vehicle counting , road traffic , data fusion , traffic control , image colour analysis , traffic operation , passing vehicle type , video signal processing , image classification , video-based traffic data collection system , object tracking , road detection setting , cross-lane vehicle , classification error reduction , passing vehicle tracking , blob space ratio , traffic engineering computing , vehicle occlusion , mean absolute error , mixed traffic condition
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