复杂交通场景中运动车辆的检测与轨迹跟踪

Detection and Trajectory Tracking of Moving Vehicles in Complicated Traffic Scene

利用数字图像技术,针对车辆检测与跟踪的3个关键环节提出了新的方法.在背景估计环节,验证像素亮度值的高斯分布特性,并据此提出背景自回归估计算法,该算法能同时适应白天和夜间两种光环境.在多个运动对象的检测环节,提出并论证一种新的只需遍历像素1次的连通成分标记算法.在车辆跟踪环节,采用Kalman滤波方法,给出状态转移矩阵和观测矩阵,并讨论初始矢量的获取模型.另外,为解决车辆跟踪过程中常出现的半遮挡问题,利用图像相似度来匹配局部图块和全图块.实际道路上的实验表明,所提出的方法实用有效,其中车辆跟踪的准确率超过95%.

This paper proposes some new methods for the three key steps of vehicle detection and trajectory tracking based on the digital image processing. In the background estimation, the Gaussian distribution hypothesis is verified and an autoregression background estimation algorithm is presented for both daytime and nighttime light-environments. In the detection of multiple moving objects, a new traversed labeled algorithm is proposed and verified,which traverses the pixels for only one time. In the tracking of vehicles, the Kalman fihering is adopted to obtain the transition and observation matrixs, and the method to get the first state vector of Kalman filter is also studied.Moreover, the image similarity is used to match the partial image to the original one, thus overcoming the semishelter usually existing in the vehicle tracking. Experimental results in real traffic scene indicate that the proposed approaches are practical and effective, with a tracking accuracy of more than 95 %.