Length-Based Vehicle Classification in Multi-lane Traffic Flow

• Published in: Transactions of Tianjin University Vol. 17; no. 5; pp. 362 - 368
• Main Author: 于洋 于明 阎刚 翟艳东
• Format: Journal Article
• Language: English
• Published: Heidelberg Tianjin University 01-10-2011
• Subjects: Engineering; Humanities and Social Sciences; Mechanical Engineering; multidisciplinary; Science; 交通流; 多车道; 差分图像; 背景减除; 视频序列; 计算成本; 车型分类; 车辆检测; image processing; horizontal projection; virtual line; background subtraction; vehicle classification
• ISSN: 1006-4982; 1995-8196
• DOI: 10.1007/s12209-011-1598-0

For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.