基于改进的三帧差和卡尔曼滤波的车辆检测方法

针对卡尔曼滤波方法在车辆检测中存在背景更新参数固定、背景建模实时性较差的问题,提出了三帧差法与自适应卡尔曼滤波算法相结合的运动车辆检测方法.先采用三帧差法快速提取车辆运动区域,再采用高斯分布确定背景更新参数,同时更新背景模型,最后将两者得到的图像相减得到最终检测结果.实验结果表明,该算法的背景更新速度较快,运动目标提取效果较好.

Vehicles Detection Based on Three-Frame-Difference and Improved Kalman Filter Method

As the existing Kalman Filter used in vehicles detection had some disadvantages of fixed background updating parameters and poor background modeling real-timeliness, proposed an vehicle detection algorithm based on three-frame-difference and adaptive Kalman filter method. Firstly extracted the vehicle motion region by three-frame-difference method, then determined the background updating parameters by Gaussian distribution and updated the background model, lastly obtained final result by the two subtracting image. Experimental result shows that the improved algorithm updates backgrounds quickly and extracts moving targets effectively.