均质雾天下摄像机动态标定的车速估计

Vehicle velocity estimation based on camera calibration under homogenous fog

在均质雾天下,利用光线传输模型中的距离信息和摄像机线性模型动态标定摄像机来计算不同天气条件下的车速,与以往研究不同的是将均质雾天加入到交通模型。该模型只包含路面以及运动前景,不需提取交通常见的先验信息或交通特征。首先,在活动图的基础上利用区域搜索算法(ASA)提取感兴趣区域,如果所选区域内像素以刃边函数的形式变化则当前天气为均质雾天;然后,根据暗原色先验原理计算场景透射率,选取路面区域具有特定透射率差的8个点标定摄像机,通过多帧取平均获得摄像机参数的准确值;最后,将行驶车辆的图像坐标变换为世界坐标得到实际速度。通过在3种不同天气条件下的车速计算实验结果,验证了本文算法的有效性。

A new algorithm for vehicle velocity calculation though automatic and dynamic camera calibration is presented in this paper.The algorithm is based on distance information in the light transmission model and a camera linear model under homogenous fog.Unlike previously published works,the factor of homogenous fog is added into our traffic model. Only road plane and moving foreground are included,while painted lines and other traffic prior information could be neglected. Three major steps construct our algorithm. First, an area search method （ASM） based on activity map recognizes the current weather condition.The current weather condition is assumed to be homogenous fog if the average pixel value from top to bo-ttom in a selected area of interest changes in the form of an edge spread function. Second,using a dark channel prior algorithm a transmission image is calculated.Intrinsic and extrinsic parameters of the camera are calculated based on the para-meter calculation formula especially for our monocular model. In this step, eight key points with special transmittance for generating necessary calculation equations are selected to calibrate the camera. The mean velocity is retrieved based on velocity calculation formula by transforming coordinates from the image plane to the world coordinate plane. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithm verifies the effectiveness of this proposed algorithm.