Vision-based Detection for Railway Level Crossing Violations
|關鍵字:||平交道;違規;電腦視覺;高斯混合模型;物件偵測;物件追蹤;level crossing;violations;computer vision;Gaussian Mixture Model;object detection;object tracking|
Recently the number of collision accidents at level crossings has increased due to road and railway user's improper/illegal behavior. In this thesis, we present a computer vision-based system to analyze the motion of vehicles, bikers and pedestrians. To analyze the uncharacteristic motions, this system first performs robust object detection, by using the Gaussian mixture models (GMM) to construct background model and segment foreground regions. Additionally, we provide some post-processing method to reduce noise and foreground fragments. Bounding box of each foreground region is then tracked using the distance between each bounding box of the object in the current frame and each object that was tracked in the previous frames. Finally, we establish some rules to check whether the behavior associated with each track is a violation. Experimental evaluations of the proposed approach show that an accuracy rate of more than 90 % can be achieved with the proposed approach.