To achieve rapid and accurate estimation of traffic noise in urban road video surveillance scenarios, a real-time noise estimation method based on computer vision is proposed. First, starting with an analysis of the mechanisms behind road traffic noise, a series of computer vision-based methods for extracting traffic flow information related to urban road noise are introduced, improving the convenience of traditional methods for extracting traffic flow data. Secondly, to address the low accuracy of traditional noise estimation algorithms, an analysis of the factors influencing urban road traffic noise is conducted. By combining traffic flow features with environmental factors, a machine learning-based model for traffic noise estimation is developed, enhancing the accuracy of urban road noise estimation. Finally, the short-term variation patterns of urban road traffic noise are analyzed, and a variable-scale feature extraction time window is determined. A complete real-time noise estimation solution is proposed, improving the real-time performance of noise estimation. Experimental results show that the proposed computer vision-based traffic flow information extraction method outperforms commonly used object detection and tracking algorithms in accurately extracting traffic noise-related information. The developed model for traffic noise estimation offers better real-time performance and accuracy compared to traditional models and provides more accurate estimates in various scenarios compared to existing machine learning-based noise estimation methods. The noise estimation methods with time scales of 3 and 10 minutes are validated, demonstrating practical application value.