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Bui, T., Wang, G., Wei, G. and Zeng, Q. (2024) Vehicle Multi-Object Detection and Tracking Algorithm Based on Improved You Only Look Once 5s Version and Deepsort. Applied Sciences, 14, Article 2690.
https://doi.org/10.3390/app14072690

has been cited by the following article:

• TITLE: Research on Vehicle Tracking Method Based on YOLOv8 and Adaptive Kalman Filtering: Integrating SVM Dynamic Selection and Error Feedback Mechanism

  AUTHORS: Liping Zheng, Hao Gou, Kaiwen Xiao, Moran Qiu

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

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.12, December 19, 2024

  ABSTRACT: 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 ([email protected] increased by approximately 3%), tracking accuracy (MOTA improved by 5%), and system robustness, providing an efficient solution for vehicle tracking in complex environments.