A Vision-Based Traffic Flow Detection Approach

Abstract

Traffic flow detection plays an important role in Intelligent Transportation System (ITS). However, the conventional traffic flow detection approaches are high cost or complex installation. In this paper, a reliably vision-based traffic flow detection approach is proposed. In this approach, Gaussian mixture model (GMM) is employed to model the dynamic background of traffic scene. Then, the binary foreground contours are extracted by image subtraction. Comparing the binary vehicle contours’ location and the current frame, the real and complete vehicles are obtained for detecting and monitoring. In the part of vehicle counting, to gather the vehicle flow parameter in each lane of the road and avoid the trouble of counting vehicles repeatedly, a discriminative method is presented to classify vehicles into different lanes. Experiment shows that a desired result can be achieved in the traffic flow detection system by the vision-based approach.