基于三维重建的交通流量检测算法

在智能交通系统中 ,道路交通流量信息实时、有效的检测是交通信息系统的关键环节 .固定相机的视频图象检测法具有诸多优点 ,为此 ,提出了一个基于知识的视频图象交通流量检测系统 ,其中 ,车辆的分割和识别是视频检测法的核心 .根据车辆具有较大的运动惯性等运动规律 ,在短时间隔内 ,可以近似认为车辆运动为刚体匀速直线运动 .在这一条件下 ,将刚体上的运动点重投影到道路平面 ,则重投速度与该点的空间位置到路面的高度具有固定的比例关系 .运动特征采用具有较好定位精度的边缘特征 ,并拟合为直线进行运动跟踪匹配 .在识别过程中 ,先假定车辆的模型及其高度 ,然后再根据重投影速度 ,重建车辆的三维空间结构 ,进行基于知识规则的假设校验 .试验结果表明 ,该方法可以较好地解决车辆视频检测中的遮挡、粘连、阴影等情况

3D Reconstruct Based Traffic Flux Detection Algorithm

In Intelligence Traffic System (ITS), the accurate detection of traffic flux at real time on road scene is very important steps. The still camera based video detection approach is one of the most important methods with much superiority to others. In this paper, a knowledge based video traffic flux detection system is presented. The traffic segmentation and recognition is the main algorithm of this system. According to the knowledge of vehicles' movement, vehicles have huge inertia, we assure that vehicles are moving at straight line in short period. With this premise, we reproject the movement of features to the road plane. The reprojected velocity of a points of the vehicle have a proportion with the height to the road plane. In the detection system, the Canny edge feature was used to fit a straight line and to match in image sequences. In the recognition stage, we assign a vehicle model and the height to the type of vehicle and reconstruct the 3D space model and verify this model with rules. The experiment results show that this algorithm is better than previous method to detect vehicle in congest, occlusion and shadow cases.