Author(s)

Liping Zheng^1*, Hao Gou², Kaiwen Xiao¹, Moran Qiu¹

¹School of Computer Science, Sichuan University Jinjiang College, Meishan, China.
²School of Automotive and Transportation, Xihua University, Yibin, China.

Vehicle tracking plays a crucial role in intelligent transportation, autonomous driving, and video surveillance. However, challenges such as occlusion, multi-target interference, and nonlinear motion in dynamic scenarios make tracking accuracy and stability a focus of ongoing research. This paper proposes an integrated method combining YOLOv8 object detection with adaptive Kalman filtering. The approach employs a support vector machine (SVM) to dynamically select the optimal filter (including standard Kalman filter, extended Kalman filter, and unscented Kalman filter), enhancing the system’s adaptability to different motion patterns. Additionally, an error feedback mechanism is incorporated to dynamically adjust filter parameters, further improving responsiveness to sudden events. Experimental results on the KITTI and UA-DETRAC datasets demonstrate that the proposed method significantly improves detection accuracy (mAP@0.5 increased by approximately 3%), tracking accuracy (MOTA improved by 5%), and system robustness, providing an efficient solution for vehicle tracking in complex environments.

Multi-Target Tracking, YOLOv8-Based Detection, Adaptive Filtering, Support Vector Machine, Error Feedback Mechanism

Zheng, L. , Gou, H. , Xiao, K. and Qiu, M. (2024) Research on Vehicle Tracking Method Based on YOLOv8 and Adaptive Kalman Filtering: Integrating SVM Dynamic Selection and Error Feedback Mechanism. Open Journal of Applied Sciences, 14, 3569-3588. doi: 10.4236/ojapps.2024.1412234.