Image Target Detection Algorithm Based on Computer Vision Technology

With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as image processing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or image processing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%- 11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. Algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, and the time complexity is also high. In experiment 2, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The highest false detection rate is only 2.2%, and the other algorithms have a false detection rate of 6.1%-11.8%, respectively. Among them, Meanshift is the highest, Gaussian mixture model is behind, and FCM takes the second place. According to Table I, Table II, Table III, the false detection rate of picture recognition in the video library is quite different from the false detection rate of pictures taken in reality. This is related to the complexity of the picture frequency shooting background environment.