RESEARCH ON TRAFFIC OBJECT TRACKING AND TRAJECTORY PREDICTION TECHNOLOGY BASED ON DEEP LEARNING
Keywords:
YOLOv8, Traffic target tracking, Trajectory prediction, Multi-target tracking, Long-and short-term memory network (LSTM)Abstract
The purpose of this study is to propose a deep learning-based solution, aiming at the problem of insufficient accuracy and real-time performance in traffic target tracking and trajectory prediction technology. We used YOLOv8 for real-time target detection, combined with the multi-target tracking track algorithm to achieve accurate tracking of traffic targets. At the same time, the trajectory prediction through the long-and short-term memory network (LSTM) can effectively deal with the dynamic changes of traffic flow. The experimental results show that the method tracks better than conventional algorithms in multiple traffic environments, with better robustness and real-time performance. Moreover, this study explores the impact of data enhancement and hyperparameter optimization on model performance, which provides new ideas and methods for the implementation of intelligent transportation system.References
[1] Kejia Zhu, Yikuan Zhang, Qiang Liu, et al. Research on Application Method of Object Detection in Video. 2022 International Conference on Cloud Computing, Big Data and Internet of Things. 2022, 10, 22.
[2] Li Liu, Wanli Ouyang, Xiaogang Wang, et al. Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision, 2020.
[3] Zhengxia Zou, Zhenwei Shi, Yuhong Guo, et al. Object Detection in 20 Years: A Survey. CoRR, 2019.
[4] Kequan Li, Yan Chen, Jiachen Liu, et al. Overview of object detection algorithms based on deep learning. Computer Engineering, 2022, 48(7): 1-12.
[5] Ghania Zidani, Djalal Djarah, Abdslam Benmakhluof, et al. Optimizing Pedestrian Tracking for Robust Perception with YOLOv8 and Deepsort. Applied Computer Science, 2024, 20(1): 72-84.
[6] Shuxin Yang, Yang Xie, Jianqing Wu, et al. CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection. ELECTRONICS, 2024, 13(14).
[7] Mukaram Safaldin, Nizar Zaghden, Mahmoud Mejdoub. An Improved YOLOv8 to Detect Moving Objects. IEEE Access, 2024, 12: 59782-59806.
[8] Ji-Yuan Ding, Wang-su Jeon, Sang-Yong Rhee, et al. DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8. 2024.
[9] Shuai Xu, Jiyou Fei, Geng Zhao, et al. CCL-YOLO: Catenary Components Location Based on YOLO and Gather-Distribute Mechanism. IEEE Access, 2024: 1.
[10] Jing Li, Zhengjun Xu, Liang Xu. Vehicle and Pedestrian Detection Method Based on Improved YOLOv4-tiny. Optoelectronics Letters, 2023, 19 (10): 623-628.