Vehicle detection and tracking based on video image processing in intelligent transportation system

• Published in: Neural computing & applications 2023, Vol.35 (3), p.2197-2209
• Main Authors: Ge, Dong-yuan, Yao, Xi-fan, Xiang, Wen-jiang, Chen, Yue-ping
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Cameras; Centroids; Color; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Contours; Data Mining and Knowledge Discovery; Image processing; Image Processing and Computer Vision; Intelligent transportation systems; Matching; Methods; Probabilistic models; Probability and Statistics in Computer Science; S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021); Saturation (color); Special Issue on 2021 Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021); Target detection; Tracking systems; Vehicles; Video compression; Vision systems
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-06979-y

As an integral part of intelligent transportation system, vehicle detection and tracking system is of great research significance and practical application value. In this paper, based on the mixed Gaussian background model, the detection target is segmented by the different methods, and the most matching target track is found by using the location information and color information of the detection target, so as to realize the vehicle tracking. The experiment results show that for the same target, the centroid distance is less than 0.2, the color distance of HSV (hue saturation value) is less than 0.3, the centroid distance of different targets is less than 0.2, the HSV distance is less than 0.3, and the rest are distributed to some extent. When the centroid distance is 0.01, 0.02, 0.03, 0.04, 0.05 and 0.06, respectively, the matching results are 250, 150, 100, 50, 25 and 10, respectively; when the HSV color distance is 0.02, 0.06, 0.1, 0.14 and 0.18, respectively, the matching results are 160, 200, 100, 80 and 50, respectively. Therefore, for the normalized distance between the same targets, including the centroid distance and HSV color, in each possible matching area, the greater the distance is, the less the distribution of matching results. Experimental verification shows that when the vehicle is detected in the detection area, the effective contour is sequentially accessed and tracked through the memory pointer, and the relatively accurate contour of the moving vehicle can be obtained through the improved Gaussian mixture model. The vehicle detection algorithm based on regional method has high real-time accuracy and strong practical value, can meet the needs of intelligent transportation system, and has strong practical value.