We introduce a multimodal vision framework for precision livestock farming,
harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This
integrated suite enables comprehensive behavioral analytics from video data
without invasive animal tagging. GroundingDINO generates accurate bounding
boxes around livestock, while HQSAM segments individual animals within these
boxes. ViTPose estimates key body points, facilitating posture and movement
analysis. Demonstrated on a sheep dataset with grazing, running, sitting,
standing, and walking activities, our framework extracts invaluable insights:
activity and grazing patterns, interaction dynamics, and detailed postural
evaluations. Applicable across species and video resolutions, this framework
revolutionizes non-invasive livestock monitoring for activity detection,
counting, health assessments, and posture analyses. It empowers data-driven
farm management, optimizing animal welfare and productivity through AI-powered
behavioral understanding.

我们介绍了一个多模态视觉框架，用于精确畜牧业，利用 GroundingDINO、HQSAM 和 ViTPose 模型的力量。此集成套件可以从视频数据中进行全面的行为分析，无需侵入性动物标记。GroundingDINO 在家畜周围生成准确的包围框，而 HQSAM 在这些框内分割个体动物。ViTPose 估计关键身体点，便于姿势和运动分析。在包括放牧、奔跑、坐立和行走活动的绵羊数据集上进行演示，我们的框架提取了宝贵的洞察力：活动和放牧模式、互动动力学和详细的姿势评估。该框架适用于各个物种和视频分辨率，为活动检测、计数、健康评估和姿势分析领域的非侵入性畜牧监测带来革命。通过基于数据驱动的行为理解，它给予农场管理以动力，并优化动物福利和生产力。