车道模型的高速公路车辆异常行为检测方法

针对高速公路上车辆的逆行、停车、轨迹异常等事件的检测问题，提出了一种基于车道模型知识的自底上向的车辆异常检测方法。首先由车道线的连续性、共线性的感知搜索出车道线和消失点，自动建立车道模型；然后在多车辆检测与跟踪时，通过目标运动位置预测和KLT特征点跟踪的方法建立表示目标区域交叠关系图，依据后验关系通过对图中节点对应目标区域的合并与拆分实现目标与实际车辆的一一对应，建立可靠的跟踪轨迹；最后基于消失点的坐标变换，计算车辆实际位置与速度，采用轨迹滑动窗口方法判断目标运动趋势并计算窗口内轨迹点平均速度，判断车辆的异常行为。实验结果表明,所提方法在不同天气、不同车流量环境中均有80%以上的事件检测率，同时算法简单，具有很好的实时性，能够适应实际高速公路智能检测设备的需求。

Abnormal behavior detection for highway vehicle based on lane model

To solve the problem of detecting highway-vehicle abnormal behavior such as retrograde motion, parking and abnormal trajectory, this paper presented a bottom-up detection algorithm based on lane model. First, the lane line and vanishing point were found out by line's continuity and collinearity, and the lane model was automatically established. Second, a region-overlap graph was established by motion prediction and KLT feature tracking to indicate region relationship of the object in the detecting and tracking process. In this graph, the corresponding relationship and reliable trajectory was made by merging or splitting the target region. The target region was ruled by posterior relationship. At last, the vehicle's location was transformed based on vanishing point. The trend of target motion was judged and its location or velocity was calculated in the lane model with sliding window to decide vehicle's behavior. The experimental results show that the proposed algorithm has more than 80% detection rate for car incident in different weather or traffic environment. This algorithm is capable to detect vehicle abnormal behavior on highway for real-time application.