用于个体层面行为分析的计算机视觉流水线：在爱丁堡猪数据集上的基准测试
A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset
AI总结：
本文提出了一种模块化流水线，利用先进的计算机视觉技术自动化群体饲养环境中的动物行为分析，通过爱丁堡猪数据集验证，实现了94.2%的总体准确率，优于现有方法。
摘要：
动物行为分析在理解动物福利、健康状况和生产力方面起着关键作用。然而，传统手动观察方法耗时、主观且扩展性有限。我们提出了一种模块化流水线，利用最先进的计算机视觉技术自动化群体饲养环境中的动物行为分析。我们的方法结合了最先进的零样本目标检测、运动感知分割和跟踪以及使用视觉变换器进行的高级特征提取，以实现稳健的行为识别。该流水线解决了动物遮挡和群体饲养场景等挑战，在室内猪监控中得到演示。我们验证了系统在爱丁堡猪行为视频数据集上的多个行为任务。我们的时序模型实现了94.2%的总体准确率，比现有方法提高了21.2个百分点。流水线展示了强大的跟踪能力，具有93.3%的身份保持（IDF1）得分和89.3%的平均精度（AP）对于目标检测。模块化设计表明有潜力适应其他情境，但需要进一步验证其他物种。开源实现提供了一种可扩展的行为监控解决方案，通过自动化、客观和持续的分析，促进精确养猪和福利评估。
英文摘要：
Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware segmentation and tracking, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios, as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with a 93.3% identity preservation (IDF1) score and an 89.3% average precision (AP) for object detection. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
