Computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be used for in-depth analysis of animals, such as examining their subtle interactive behaviors, from videos and images. However, existing deep-learning-based instance segmentation methods have been mostly developed based on public datasets, which largely omit heavy occlusion problems; therefore, these methods have limitations in real-world applications involving object occlusions, such as farrowing pen systems used on pig farms in which the farrowing crates often impede the sow and piglets. In this paper, we adapt a Center Clustering Network originally designed for counting to achieve instance segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M), and Remain-Centers-to-Mask (RC2M) algorithms. In all, 4,600 images were extracted from six videos collected from three closed and three half-open farrowing crates to train and validate our method. CClusnet-Inseg achieves a mean average precision (mAP) of 84.1 and outperforms all other methods compared in this study. We conduct comprehensive ablation studies to demonstrate the advantages and effectiveness of core modules of our method. In addition, we apply CClusnet-Inseg to multi-object tracking for animal monitoring, and the predicted object center that is a conjunct output could serve as an occlusion-resistant representation of the location of an object.

翻译：计算机视觉为监测动物行为、健康与福祉提供了新方法。实例分割作为计算机视觉中检测特定动物个体的高精度技术，可用于深度分析动物行为，例如从视频和图像中研究动物间的细微互动。然而，现有基于深度学习的实例分割方法多基于公开数据集开发，普遍忽略了严重遮挡问题；因此在涉及物体遮挡的实际应用中存在局限性，例如猪场产床系统中，限位栏常阻碍母猪与仔猪的交互。本文改编了原本用于计数的中心聚类网络（Center Clustering Network）以实现实例分割，命名为CClusnet-Inseg。具体而言，CClusnet-Inseg利用每个像素预测物体中心，并基于聚类结果追踪这些中心以形成掩膜。该系统包含分割网络、中心偏移向量图、基于密度的空间聚类算法（DBSCAN）、中心到掩膜算法（C2M）及剩余中心到掩膜算法（RC2M）。我们从三个封闭式与三个半开放式产床采集的六段视频中提取了4600张图像进行模型训练与验证。CClusnet-Inseg的平均精度（mAP）达到84.1，优于本研究比较的所有其他方法。通过全面的消融实验，我们验证了核心模块的优势与有效性。此外，我们将CClusnet-Inseg应用于动物监测中的多目标跟踪，其联合输出的预测物体中心可作为物体位置的抗遮挡表征。