基于改进均值建模的自适应三帧差分算法研究

针对实际视频图像中复杂的背景环境（光照变化、有微动物体等），运动目标检测算法不易提取出完整的运动目标，提出了基于改进均值背景模型的自适应三帧差分算法。该算法利用前k帧建立的均值背景模型作为三帧差分法的中间帧，再采用三帧差分法，并对差分结果选取自适应阈值来二值化，将两个检测出的目标进行“与”运算，最后通过形态学处理、滤波等，获得运动目标的真实位置。最后试验结果表明，提出的算法能够适应比较复杂的背景环境，不易受光照变化或其他微小变化的影响，又能有效克服空洞和边缘丢失的现象，并且检测准确率更高，适用于无人监控环境。

Research on adaptive three frame difference algorithm based on improved mean modeling

In view of the complex background environment (light changes, micro-animals, etc.) in real video images, moving target detection is not easy to extract complete moving targets, and an adaptive three-frame difference algorithm based on improved mean modeling is proposed. The algorithm uses the mean background model established by the previous k-frame as the intermediate frame of the three-frame difference method, and then uses the three-frame difference method, and selects the adaptive threshold to binarize the difference result. An AND operation is performed on the two detected targets, followed by morphological processing, filtering, etc., and then the true position of the moving target is obtained. Finally, the experimental results show that the proposed algorithm can adapt to the more complex background environment, is not susceptible to illumination changes or other minor changes, and can effectively overcome the phenomenon of void and edge loss, and has higher detection accuracy, suitable for unattended monitoring. surroundings.