Machine Vision Based The Spatiotemporal Information Identification of The Vehicle

Abstract

Accurately identifying the vehicle load on the bridge plays a vital role in structural-stress analysis and safety evaluation. Also, extracting the spatiotemporal information of the vehicle’s is crucial for identifying the vehicle load. This study aimed to propose a vehicle spatiotemporal information-identification method based on machine-vision technology. First, digital video surveillance cameras were installed in the front and on the side of the monitoring section to capture real-time videos of vehicles passing through the monitoring section. The background-difference method was used to detect vehicles based on the frontal video. Subsequently, the transverse position was evaluated according to the distance between the vehicle’s license plate and the lane line. Other vehicle parameters, including the vehicle’s speed, the number of axles and the wheelbase, were identified based on the lateral video and the auxiliary lines with a known distance. Second, a laboratory model experiment and multiple field tests under different scenes were carried out to validate the efficiency and accuracy of the proposed method. The results indicated that the average identification errors of wheelbase for the model experiment and the field tests were all 1.12% and those of the vehicle’s speed were 1.25% and 1.35, respectively. Also, the average deviations of the lateral position were 2.57 mm and 2.69 cm, respectively. The variances of the identified error of the three parameters for the field tests were 0.78%, 1.83 cm and 0.54%, respectively. This verified that the proposed method has high accuracy, reliability and good anti-noise performance. KEYWORDS: Machine vision, Spatiotemporal information, Load identification, Orthotropic deck, Bridge engineering.