Aiming at the challenges of pedestrian tracking, such as local occlusion and frequent ID change, which is frequently encountered in the complex and variable traffic environment, a multi-target pedestrian tracking method integrating YOLOv8 and DeepSORT (Simple Online and Realtime Tracking with a Deep Association Metric) is proposed. Firstly, in the detection stage, to enhance the ability of capturing the feature information for target pedestrians in dense traffic scenarios, YOLOv8 algorithm is selected, which is renowned for its efficient small-scale feature processing capability, ensuring the accuracy and speed of detection. Secondly, to fulfill the requirement of real-time tracking, OSNet (Omni-Scale Network) is introduced as the feature extraction network based on DeepSORT. Through the multi-scale dynamic fusion strategy, OSNet provides a richer and more accurate information basis for subsequent tracking. Thirdly, in view of the limitations of traditional Kalman filtering in nonlinear motion trajectory prediction, an innovative filter smoothing Kalman algorithm (FSA) is designed, which can flexibly adjust filtering parameters and effectively cope with the uncertainty of pedestrian movement in traffic scenes, significantly enhancing the accuracy of prediction. Additionally, to improve the stability and accuracy of data matching in the tracking process, the original intersection over union (IOU) association matching mechanism of DeepSORT is replaced with the improved complete-intersection over union (CIOU) algorithm. CIOU not only considers the degree of overlap between objects but also incorporates geometric information such as shape and size, effectively reducing the rate of missed and false detection. Finally, to further mitigate the impact of multiple noises on tracking performance, the trajectory feature extractor (GFModel) is introduced. The model combines local details with global context information through average pooling technology to achieve accurate tracking and prediction of the target pedestrian trajectory. Experimental results demonstrate that the proposed method achieves 77.9% tracking accuracy while maintaining a processing speed of 55.8 frame per second (FPS), which fully meets the demand for efficient and accurate tracking in actual complex traffic environment.