Abstract In animal agriculture, animal health directly influences productivity. For dairy cows, many health conditions can be evaluated by trained observers based on visual appearance and movement. However, to manually evaluate every cow in a commercial farm is expensive and impractical. This study introduces a video-analytic system which automatically detects the cow structure from captured video sequences. A side-view cow structural model is designed to describe the spatial positions of the joints (keypoints) of the cow, and we develop a system using deep learning to automatically extract the structural model from videos. The proposed system can detect multiple cows in the same frame and provides robust performance for the body region under practical challenges like obstacles (fences) and poor illumination. Compared to other object detection methods, this system provides better detection results and successfully isolates the body keypoints of each cow even when the cows are close to each other.

中文翻译：

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用于检测奶牛结构的视频分析系统

摘要 在畜牧业中，动物健康直接影响生产力。对于奶牛，许多健康状况可以由训练有素的观察员根据视觉外观和运动进行评估。然而，手动评估商业农场中的每头奶牛既昂贵又不切实际。本研究介绍了一种视频分析系统，该系统可从捕获的视频序列中自动检测奶牛的结构。侧视奶牛结构模型旨在描述奶牛关节（关键点）的空间位置，我们开发了一个使用深度学习自动从视频中提取结构模型的系统。所提出的系统可以检测同一帧中的多头奶牛，并在障碍物（围栏）和光照不足等实际挑战下为身体区域提供稳健的性能。