标题: 基于视觉分析之铁路平交道违规事件侦测
Vision-based Detection for Railway Level Crossing Violations
作者: 张益彰
Chang, Yi-Chang
庄仁辉
Chuang, Jen-Hui
多媒体工程研究所
关键字: 平交道;违规;电脑视觉;高斯混合模型;物件侦测;物件追踪;level crossing;violations;computer vision;Gaussian Mixture Model;object detection;object tracking
公开日期: 2012
摘要: 近年来由于用路人的不当行为造成铁路平交道事故频传。本论文提出一个基于电脑视觉的平交道路口汽车、机车及行人行为分析系统。此系统首先利用高斯混合模型进行前景侦测,而后对于侦测的结果进行ㄧ些后处理以消除杂讯以及减少前景破碎。接着我们衡量两张影像前景的外接矩形之距离关系以进行追踪,并且透过几条判定违规准则的订定,来检视每条代表物件行为的轨迹是否异常(违规)。经由几个真实场景实验后的统计,本论文提出的铁路平交道违规分析系统之正确率可达90%以上。
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.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT070056643
http://hdl.handle.net/11536/72619
显示于类别:Thesis