Department of Animal Science, University of California, Davis, CA 95616, USA.
Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
J Anim Sci. 2022 Jun 1;100(6). doi: 10.1093/jas/skac147.
Precision livestock farming has become an important research focus with the rising demand of meat production in the swine industry. Currently, the farming practice is widely conducted by the technology of computer vision (CV), which automates monitoring pig activity solely based on video recordings. Automation is fulfilled by deriving imagery features that can guide CV systems to recognize animals' body contours, positions, and behavioral categories. Nevertheless, the performance of the CV systems is sensitive to the quality of imagery features. When the CV system is deployed in a variable environment, its performance may decrease as the features are not generalized enough under different illumination conditions. Moreover, most CV systems are established by supervised learning, in which intensive effort in labeling ground truths for the training process is required. Hence, a semi-supervised pipeline, VTag, is developed in this study. The pipeline focuses on long-term tracking of pig activity without requesting any pre-labeled video but a few human supervisions to build a CV system. The pipeline can be rapidly deployed as only one top-view RGB camera is needed for the tracking task. Additionally, the pipeline was released as a software tool with a friendly graphical interface available to general users. Among the presented datasets, the average tracking error was 17.99 cm. Besides, with the prediction results, the pig moving distance per unit time can be estimated for activity studies. Finally, as the motion is monitored, a heat map showing spatial hot spots visited by the pigs can be useful guidance for farming management. The presented pipeline saves massive laborious work in preparing training dataset. The rapid deployment of the tracking system paves the way for pig behavior monitoring.
精准畜牧养殖在猪肉产业需求不断增长的背景下已成为一个重要的研究热点。目前，该养殖实践主要通过计算机视觉（CV）技术来实现，该技术仅通过视频记录自动监测猪的活动。自动化是通过提取图像特征来实现的，这些特征可以引导 CV 系统识别动物的身体轮廓、位置和行为类别。然而，CV 系统的性能对图像特征的质量很敏感。当 CV 系统部署在多变的环境中时，由于在不同光照条件下特征不够通用，其性能可能会下降。此外，大多数 CV 系统都是通过监督学习建立的，这需要在训练过程中对地面实况进行大量标注工作。因此，本研究开发了一个半监督流水线 VTag。该流水线专注于猪活动的长期跟踪，不需要任何预先标记的视频，但需要少量的人工监督来建立 CV 系统。该流水线可以快速部署，因为跟踪任务只需要一个顶视图 RGB 摄像机。此外，该流水线还作为一个软件工具发布，具有友好的图形界面，可供普通用户使用。在所呈现的数据集，平均跟踪误差为 17.99 厘米。此外，通过预测结果，可以估计猪在单位时间内的移动距离，用于活动研究。最后，由于可以监测运动，显示猪访问的空间热点的热图可为养殖管理提供有用的指导。所提出的流水线节省了大量在准备训练数据集方面的繁琐工作。跟踪系统的快速部署为猪行为监测铺平了道路。
