多对象行人跟踪使用改进的YOLOv8和OC-SORT
1College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究通过使用先进技术改进YOLOv8物体检测模型来增强自动驾驶的行人跟踪. 优化的模型实现了更高的准确性和效率,大大提高了基准数据集的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 多对象行人跟踪对于自动驾驶系统至关重要.
- 准确的环境感知依赖于强大的行人跟踪.
研究的目的:
- 为自动驾驶开发一个全面的行人跟踪方法.
- 提高行人检测和跟踪算法的效率和准确性.
主要方法:
- 在Crowdhuman数据集上训练了一种改进的YOLOv8物体检测模型.
- 集成了包括softNMS,GhostConv和C3Ghost模块在内的先进技术.
- 增强的YOLOv8模型与用于行人跟踪的OC-SORT跟踪算法相结合.
主要成果:
- 精度增加了3.38%,mAP@0.5:0.95增加了3.07%,参数减少了39.98%.
- 模型尺寸减少了37.1%,从而实现了更有效的检测.
- 在MOT17上,HOTA达到了49.92%和MOTA达到了56.55%;在MOT20上,HOTA达到了48.326%和MOTA达到了61.077%.
结论:
- 拟议的方法显著提高了行人检测的准确性和效率.
- 集成的YOLOv8和OC-SORT系统在复杂的跟踪场景中表现出卓越的性能.
- 这项工作有助于更可靠的自动驾驶感知系统.
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