改进了基于DeepSORT的对象跟踪在雾天气中的AVs使用语义标签和融合外观功能网络
Isaac Ogunrinde1, Shonda Bernadin1
1Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种增强的多对象跟踪模型,可以提高雾中的性能. 新方法提高了准确性和速度,同时减少了身份开关,以便在恶劣天气下更好地跟踪物体.
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:
- 雾在安全关键应用中显著降低了对象检测和跟踪性能.
- 现有的多对象跟踪 (MOT) 模型,如DeepSORT,由于特征地图的限制,在雾的条件下难以获得准确性和速度.
研究的目的:
- 开发一个改进的多对象跟踪模型,提高准确性和速度,特别是在雾的天气条件下.
- 为了应对特征地图细节丢失和不利可见性目标不匹配的挑战.
主要方法:
- 提出了一种相机-雷达融合网络 (CR-YOLOnet),用于增强对象检测.
- 引入了一个新的外观功能网络,利用GhostNet生成更丰富的功能,降低计算成本.
- 集成了一个细分模块,将语义信息纳入特征地图.
主要成果:
- 与YOLOv5 + DeepSORT相比,实现了35.15%的多对象跟踪精度增加和32.65%的精度增加.
- 提高了37.56%的跟踪速度,减少了46.81%的身份开关.
- 在具有挑战性的雾环境中表现出卓越的性能.
结论:
- 提议的改进的DeepSORT算法显著提高了在雾中多对象跟踪的性能.
- 摄像机-雷达融合,GhostNet和语义细分的集成为在恶劣天气中运行的真实世界的自主系统提供了强大的解决方案.
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