目的 如何使快速性与完整性达到平衡是运动目标检测的关键问题。现有的满足快速性的算法容易受到光照的影响，对动态环境的适应能力较弱，获取的目标信息不完整，导致空洞问题的产生。而具有较高完整性的算法复杂度高，运算速度慢，实时性差。为此，本文提出基于自适应混合高斯建模的3帧差分算法。方法 利用3帧差分运算简单、可扩展性强、抗干扰能力好的特性，对视频图像进行目标轮廓的提取。针对3帧差分运算导致目标内部信息提取不完整的问题，采用学习率自适应调整的混合高斯背景差分，在模型创建之初，通过较快的模型更新速率，增加背景模型的迭代次数，消除物体运动造成的"鬼影"。在背景模型中的干扰信息消除之后，以目标像素及相邻8像素在当前帧与背景模型中的差异度为依据调整学习率，实现背景模型的自适应修正，增加目标图像的完整性；同时，通过删除冗余的高斯分布，降低算法复杂度。为进一步确保目标边缘的完整及连续，采用边缘对比差分算法，使参与运算的帧数依据目标的运动速度自适应选取，以降低背景点的误判率，使边缘信息尽可能地连续、完整。结果 本文算法获取的目标信息完整，且边缘平滑。在提升检测率的同时保证较高的准确率，达到了95.23%，所获目标的完整度提高了28.95%；与传统混合高斯算法相比，时间消耗降低了29.18%，基本达到实时性要求。与基于混合高斯建模的背景差分法（BD-GMM）和基于边缘对比的3帧差分法（TFD-EC）相比，本文算法明显占优。结论 实验结果表明，本文算法可以有效抑制动态环境的干扰，降低算法复杂度，既保证实时性，又具有较好的完整性，可广泛应用于智能视频监控、军事应用、工业检测、航空航天等领域。

Objective Moving target detection is an important branch of image processing and computer vision, and it is also a core part of intelligent monitoring systems. Its main content is to observe the entire scene in the video sequences and find the moving targets. Therefore, the main purpose of moving target detection is to extract the moving target from the video sequences effectively and obtain the feature information of the moving target, such as color, shape, and contour. Extracting moving targets is the process of target and background classification. The process finds the difference by successive sequences of images and extracts the differences owing to the motion of the object to obtain the desired target. Moving target detection requires fast acquisition of moving targets in the video image and, as much as possible, to ensure the integrity of the acquired moving targets. Thus, speed and integrity are two key indicators of moving target detection algorithms. In terms of rapidity, algorithms are required to have lower complexity and can detect moving targets in real time. The existing algorithms that satisfy speed are easily affected by illumination, have weak adaptability to the dynamic environment, and the acquired target information is incomplete, thereby resulting in a hole problem. The internal integrity of the target and the integrity of the target contour are required, thereby indicating that the internal information of the moving target can be fully obtained, and the phenomenon of missed detection caused by the misidentification of the foreground area as the background in the detection is eliminated. At the same time, the target edges are as continuous and smooth as possible. However, the algorithm with improved integrity has high complexity, slow operation speed, and poor real-time performance. Therefore, achieving the balance between speed and integrity has become a key issue in moving target detection, causing the algorithm to have high extraction efficiency while fully extracting the internal information and contour of the target. Method This study proposes a three-frame difference algorithm based on adaptive Gaussian mixture modeling. To ensure the real-time performance of the algorithm, this study relies on the three-frame difference operation, which is simple, extensible, and has good anti-interference ability to extract the target contour of the video image. The operation can improve the detection efficiency of the algorithm. For the problem that the three-frame difference operation leads to incomplete extraction of the internal information of the target, the Gaussian mixture background difference adaptively adjusted by the learning rate is used. The difference achieves an adaptive update of the background model by setting the frame number threshold and adopting different learning rates before and after the threshold. At the beginning of the model creation, the rate of iteration of the background model is increased by the faster update rate of the model, and the "ghosting" caused by the motion of the object is eliminated. After the interference information in the background model is eliminated, the learning rate is adjusted based on the difference between the target pixel and adjacent eight pixels in the current frame and the background model, thereby implementing adaptive correction of the background model and solving the problem of misjudgment and loss of targets generated during the model update process. The approach can increase the integrity of the target image. At the same time, to speed up the Gaussian mixture modeling, the model redundancy decision strategy is adopted to determine the weight and priority of the Gaussian distributions, and the redundant Gaussian distributions are deleted to avoid the time consumption caused by the redundancy models in the matching. Ultimately, the balance between algorithm integrity and algorithm real-time are achieved. To further ensure the integrity and continuity of the target edge, we use the edge contrast difference algorithm, which is based on the target edge detected by the Canny operator. The number of frames participating in the edge contrast operation is adaptively selected based on the target motion speed, thereby decreasing the false positive rate of the background point and making the edge information as continuous and complete as possible. Result Subjective and objective evaluation methods are combined on the experimental results. Subjectively, the background difference based on Gaussian mixture modeling (BD-GMM), the three-frame difference based on edge contrast (TFD-EC), and the proposed algorithm are used to detect single-target and multi-target video in different backgrounds. The results show that the target information obtained by the algorithm are complete and the edges are smooth. Objectively, the proposed algorithm improves the detection rate while ensuring a high accuracy rate of 95.23%, and the integrity of the target is improved by 28.95%. These values are significantly higher than those of other algorithms. In terms of speed, the time consumption is reduced by 29.18% compared with that of the traditional Gaussian mixture algorithm, thereby meeting the real-time requirements. Compared with the BD-GMM and TFD-EC algorithms, both subjective and objective, the proposed algorithm is superior to the two algorithms. Conclusion The experimental results show that because the algorithm adopts Gaussian mixture background modeling based on adaptive learning rate, it can effectively suppress the interference of a dynamic environment and decrease the complexity of the algorithm. The three-frame difference algorithm based on edge comparison ensures the timeliness of the algorithm and integrity of the target edge. Therefore, the proposed algorithm ensures real-time performance, has good integrity, and can be widely used in fields such as intelligent video surveillance, military applications, industrial inspection, and aerospace.