改进了基于StrongSORT的无人机到地面多目标跟踪算法
Xinyu Cao1, Zhuo Wang1, Bowen Zheng1
1School of Computer and Control Engineering, Northeast Forestry University, Harbin 150006, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了使用无人机 (UAV) 实时多目标跟踪的优化框架. 该系统显著提高了复杂环境中的地面机器人的跟踪精度和速度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 对于空中监测和侦察至关重要.
- 基于视觉的多目标追踪对无人机构成重大挑战,特别是在动态环境中.
- 现有的多目标跟踪算法与目标封闭,摄像头动和小目标大小等问题作斗争.
研究的目的:
- 开发一个强大的框架,用于实时多目标跟踪使用无人机的地面机器人.
- 提高检测和重新识别网络的性能,以改善实时目标检测.
- 解决当前跟踪算法的局限性,确保在具有挑战性的条件下可靠的跟踪.
主要方法:
- 利用YOLOv5n检测算法在定制数据集上进行训练.
- 实现了StrongSORT跟踪算法,整合了优化的YOLOv5n模型权重.
- 专注于优化检测和重新识别网络,以提高实时性能.
主要成果:
- 实现了ID开关 (IDSW) 的六倍减少.
- 增加了7.93%的IDF1得分,并减少了30.28%的错误阳性 (FP).
- 达到每秒38的追踪速度,满足实时要求.
结论:
- 开发的框架有效地满足无人机平台的实时跟踪要求.
- 优化的YOLOv5n和StrongSORT集成为动态多目标跟踪提供了可靠的解决方案.
- 这一进步支持在复杂地形上增强空中侦察和监控能力.
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