基于跟踪轨迹的车辆异常行为检测

为提高ITS(Intelligent Traffic System)交通事件管理的智能性, 提出基于跟踪轨迹的车辆异常行为检测,分为目标检测跟踪、轨迹分析处理和车辆行为分析3 个步骤。首先利用三帧差法对目标进行初始定位, 采用基于Kalman 预测器的改进跟踪算法对车辆进行跟踪; 然后提出采用最小二乘法自适应分段直线拟合算法对目标跟踪获得的运动轨迹进行快速拟合; 最后结合运动方向变化率和速度变化率两个参数建立车辆异常行为检测模型。实验结果表明, 在道路监控视频中, 该算法能快速准确检测急刹车、急转弯和急转弯刹车等车辆异常行为。

Vehicle Abnormal Behavior Detection Based on Trajectory Tracking

In order to improve the intelligence of ITS(Intelligent Traffic System), abnormal vehicles behaviors detection by means of the vehicles trajectory is proposed. The process is divided into three steps: target detection, tracking, vehicles trajectory analysis and vehicles behavior analysis. Firstly, the three frame difference method is used to achieve initially target location and the improved tracking algorithm based on Kalman predictor is used to track vehicle; then, the adaptive segmentation linear fitting algorithm based on the least squares method is used to achieve vehicle trajectory fitting quickly; finally, two parameters including the rate of velocity variation and the rate of direction variation are used to establish vehicle abnormal behavior detection model. Experiment result shows that the three high dangerous vehicle behaviors in the road surveillance videos can be detected quickly and effectively by the algorithms: sharp brake, sharp turn, and sharp turn brake.