Video‐based traffic data collection system for multiple vehicle types. Issue 2 (1st March 2014)

• Record Type: Journal Article
• Authors: Li, Shuguang; Yu, Hongkai; Zhang, Jingru; Yang, Kaixin; Bin, Ran
• Is Part Of: IET intelligent transport systems. Volume 8:Issue 2(2014)
• Page Start: 164
• Page End: 174
• Publication Date: 2014-03-01
• Subjects: image classification -- image colour analysis -- image fusion -- road traffic -- traffic engineering computing -- video signal processing -- object tracking; video‐based traffic data collection system -- pavement design -- traffic operation -- traffic control -- vehicle type -- passing vehicle tracking -- passing vehicle classification -- mixed traffic condition -- passing vehicle type -- passing vehicle speed -- colour image‐based adaptive background subtraction -- shadow removal process -- road detection setting -- vehicle counting -- cross‐lane vehicle -- blob space ratio -- data fusion -- classification error reduction -- vehicle occlusion -- mean absolute error -- speed detection; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2012.0099
• Languages: English
• ISSNs: 1751-956X

Abstract

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.