改进混合高斯模型的电网检修人员行为检测

针对运动目标检测，当前算法具有一定的适用性和局限性，以及检测信息不完整等问题，在帧间差分法和混合高斯模型的基础上，提出了一种改进的混合高斯模型的目标检测算法，用来解决帧间差分法造成的运动目标背景轮廓不完整的问题.该方法是在传统的混合高斯模型的基础上，在一定帧数内，检查所有的高斯分布的权重，对满足条件的高斯分布进行删除操作，最终得到轮廓较为清晰的运动目标.实验结果表明，本文算法充分的考虑了背景人物对于运动目标监测产生的影响，实验过程中使用了真实的电网数据，从而说明该算法在准确度上相比于其他算法提高了3.37%，具有更好的准确性和对环境的适应能力.

Behavior Detection of Grid Maintenance Personnel Based on Improved Mixed Gaussian Model

Aiming at moving object detection, the current algorithm has certain applicability and limitations, as well as incomplete detection information. Based on the inter-frame difference method and hybrid Gaussian model, an improved hybrid Gaussian model target detection algorithm is proposed. To solve the problem of incomplete background contours of moving targets caused by the inter-frame difference method. This method is based on the traditional mixed Gaussian model, within a certain number of frames, checks the weights of all Gaussian distributions, deletes the Gaussian distributions that meet the conditions, and finally obtains a moving target with a clearer outline. The experimental results show that the algorithm in this study fully considers the influence of background characters on moving target monitoring. During the experiment, real grid data is used. The result shows that the proposed algorithm is 3.37% more accurate than other algorithms, with better accuracy and better adaptability to the environment.