关键词: 车辆检测, 数字图像处理, 背景估计, 连通像素标记, 轨迹跟踪, Kalman滤波

Abstract:

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 %.

Key words: detection of vihicle, digital image processing, background estimation, connected pixels labeling, trajectory tracking, Kalman filtering