牛检测和跟踪系统使用多功能跟踪算法
Cho Cho Mar1, Thi Thi Zin2, Pyke Tin1
1Graduate School of Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.
Scientific reports
|October 13, 2023
概括
这项研究引入了一种用于牛牧场管理的新型视频分析系统,可通过深度学习和图像处理准确识别和跟踪个别牛. 该方法通过克服视觉识别挑战来增强健康监测和分娩预测.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 基于视频的监测对牛的健康和福利至关重要,有助于行为分析和分娩预测.
- 传统的基于传感器的方法可能会给牛造成压力;视频摄像机提供了一个非侵入性的替代方案.
- 在视频分析中,识别和跟踪单个牛,特别是那些外表相似的牛,仍然是一个重大挑战.
研究的目的:
- 开发一个强大的基于视频的系统,用于在农场环境中识别和跟踪单个牛.
- 解决相似的牛品种视觉识别的局限性.
- 提高牛群健康和生殖管理监测的准确性.
主要方法:
- 开发了一个结合深度学习和图像处理技术的系统,包括数据预处理,牛检测和牛跟踪.
- 牛检测利用了一个实例细分网络.
- 牛追踪采用多重对象追踪 (MOT) 算法,集成位置,外观 (颜色时刻,共发生矩阵) 和深度特征 (CNN特征).
主要成果:
- 拟议的系统有效地检测和跟踪个人牛,即使是外表相似的牛.
- 多种功能 (位置,外观,深度功能) 的集成显著提高了跟踪性能.
- 实验结果表明,在处理多个对象跟踪挑战时,可靠的性能.
结论:
- 开发的深度学习和图像处理系统为个人牛的识别和跟踪提供了可靠的解决方案.
- 这种基于视频的非侵入性方法通过改善健康监测和分娩预测来增强牛场管理.
- 该系统为基于传感器的方法提供了一个有希望的替代方案,用于监测牛的行为和福祉.
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