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 tracking and segmentation, 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 93.3% identity preservation score and 89.3% object detection precision. 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.

翻译：动物行为分析在农业环境中对于理解动物福利、健康状况及生产力具有关键作用。然而，传统人工观察方法耗时、主观且可扩展性有限。本文提出一种模块化流程，利用开源前沿计算机视觉技术实现群养环境下动物行为分析的自动化。该方法融合了零样本目标检测、运动感知跟踪与分割的先进模型，并采用视觉Transformer进行高级特征提取，以实现鲁棒的行为识别。该流程解决了包括动物遮挡和群养场景在内的挑战，如室内猪只监控所示。我们在爱丁堡猪行为视频数据集上针对多项行为任务验证了系统性能。其时序模型整体准确率达到94.2%，较现有方法提升21.2个百分点。该流程展现出鲁棒的跟踪能力，身份保持分数达93.3%，目标检测精度达89.3%。模块化设计表明其具备适应其他场景的潜力，但需跨物种进一步验证。开源实现为行为监测提供了可扩展解决方案，通过自动化、客观且持续的分析，为精准养猪与福利评估作出贡献。