基于YOLOv8和改进的DeepSORT的多目标行人跟踪方法
Wenshun Sheng1, Jiahui Shen1, Qiming Huang1
1Pujiang Institute, Nanjing Tech University, Nanjing 211200, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
本研究介绍了YOFGD,这是一个结合YOLOv8和DeepSORT的行人跟踪系统,用于提高复杂交通中的准确性. 该方法增强了特征提取和预测,以获得可靠的现实世界性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 在复杂的交通场景中,行人跟踪面临着诸如阻塞和ID切换等挑战.
- 现有的方法经常在动态目标重新识别和轨迹预测准确性方面扎.
研究的目的:
- 开发一种强大的多目标行人跟踪方法 (YOFGD),以解决现实交通中的封闭和ID转换问题.
- 提高行人检测和跟踪系统的准确性和实时性能.
主要方法:
- 使用You Only Look Once版本8 (YOLOv8) 进行增强的功能提取和全尺度网络 (OSNet) 进行实时跟踪.
- 集成了自适应性忘记平滑卡尔曼过算法 (FSA) 和完整的交叉点在欧盟 (CIOU) 上,以改进数据关联.
- 开发了一个通用轨迹特征提取器模型 (GFModel) 来合并本地和全球轨迹信息.
主要成果:
- 通过YOFGD方法实现了77.9%的追踪精度.
- 该系统的实时处理速度为每秒55.8 (FPS).
- 在减少行人遗漏和错误检测率方面观察到显著的改善.
结论:
- YOFGD有效地应对复杂交通场景中的挑战,提供高精度和实时性能.
- 拟议的YOLOv8,改进的DeepSORT,OSNet,FSA和GFModel的融合为行人跟踪提供了一个强大的解决方案.
- 该系统的性能满足了实际现实应用的要求.
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