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相关实验视频

Updated: Jun 6, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

Published on: June 5, 2019

基于改进的字节跟踪,对集体养猪的行为跟踪和分析.

Shuqin Tu¹, Haoxuan Ou¹, Liang Mao²

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

Animals : an open access journal from MDPI

|November 27, 2024

概括

Pig-ByteTrack自动化了猪行为分析,用于在智能农业中早期检测健康问题. 这种方法通过先进的追踪和检测技术来增强猪福利监测.

关键词:

在Pig-ByteTrack中使用行为分析算法行为分析算法长时间视频跟踪.多个对象跟踪 (MOT)

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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

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Published on: April 8, 2019

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Last Updated: Jun 6, 2025

Noninvasive, In-pen Approach Test for Laboratory-housed Pigs

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Published on: June 5, 2019

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

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Published on: April 8, 2019

科学领域:

农业科学农业科学
计算机视觉计算机视觉
动物科学动物科学

背景情况:

每日对集体养猪猪的行为分析对于在智能养猪中早期发现健康问题和福利问题至关重要.
需要自动化监测系统来有效分析猪行为,并及时提供见解.

研究的目的:

开发一种自动化方法,Pig-ByteTrack,用于监测和分析群体养猪的猪行为.
用先进的计算机视觉技术及时检测健康问题并改善动物福利.

主要方法:

猪-ByteTrack方法包括目标检测,多对象跟踪 (MOT) 和行为时间计算.
猪检测和行为识别使用了YOLOX-X检测模型,其次是用于跟踪的Pig-ByteTrack.
通过使用1分钟和10分钟的视频数据集来评估性能,测量诸如高顺序跟踪精度 (HOTA) 和多对象跟踪精度 (MOTA) 等指标.

主要成果:

在1分钟的视频中,Pig-ByteTrack实现了高精度:72.9%的HOTA,91.7%的MOTA,89.0%的IDF1和41个ID开关.
与ByteTrack和TransTrack等现有方法相比,观察到显著的改进.
在10分钟的视频中,Pig-ByteTrack实现了59.3%的HOTA,89.6%的MOTA,53.0%的IDF1和198个ID开关.

结论:

猪-ByteTrack方法在猪行为识别和跟踪方面表现出有效性.
这项技术为智能养殖环境中的猪群健康和福利监测提供了宝贵的技术支持.
自动行为分析是提高动物福利和农场管理的关键.