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基于多对象跟踪方法的群体猪的跟踪和行为分析.

Shuqin Tu1,2, Jiaying Du1, Yun Liang1,2

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

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|October 16, 2024
PubMed
概括
此摘要是机器生成的。

一个新的智能养殖系统使用YOLOv8和OC-SORT (V8-Sort) 准确跟踪猪行为,改善健康和福利监测. 这种强大的多对象跟踪方法克服了诸如阻塞和猪聚类等挑战,以进行可靠的长期分析.

关键词:
这就是OC-SORT.这就是YOLOv8的意义.行为分析行为分析.在集体养的猪中.猪行为跟踪 猪行为跟踪

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科学领域:

  • 农业技术 农业技术
  • 动物行为科学 动物行为科学
  • 计算机视觉 计算机视觉

背景情况:

  • 在集体养环境中监测猪健康和福利对于智能养殖至关重要.
  • 传统方法面临着诸如可变照明,遮蔽和猪集群等挑战,导致跟踪错误.
  • 需要准确的,自动化的行为跟踪来改善畜牧管理和早期疾病检测.

研究的目的:

  • 开发一种强大的多对象跟踪 (MOT) 方法,用于自动监测集体养猪猪的行为.
  • 解决跟踪猪的常见挑战,包括可变的照明,遮蔽和猪集群.
  • 为实时行为跟踪提供可靠的解决方案,增强猪健康和福利管理.

主要方法:

  • 利用YOLOv8实时检测猪在具有挑战性的条件下的行为分类.
  • 采用OC-SORT进行强大的多对象跟踪,有效处理猪集群和封闭.
  • 开发了一个自动行为分析算法,集成检测和跟踪数据进行长期监控.

主要成果:

  • 与JDE,Trackformer和TransTrack相比,V8-Sort方法在跟踪猪的数据集上取得了更高的性能.
  • 获得高的HOTA,MOTA和IDF1分数 (例如,在1分钟的视频中达到82.0%,96.3%,96.8%).
  • 该系统准确地记录了特定猪行为的持续时间,提供了对健康和福利的见解.

结论:

  • 拟议的YOLOv8 + OC-SORT (V8-Sort) 方法在猪行为识别和跟踪方面提供了出色的性能.
  • 这项技术为猪养殖中异常检测和健康状况监测提供了必要的技术支持.
  • 该系统有助于在智能农业环境中改善动物福利和有效管理.