SwinePose: A Lateral-View Benchmark Dataset for Pig Pose Estimation and Gait Analysis

SwinePose is the first publicly available lateral-view benchmark dataset for markerless pig pose estimation and gait analysis. It comprises 3,778 annotated frames extracted from 85 video clips recorded across three commercial pig farms (Durofarm1, Sunds1, Ikadan1), with 15 anatomically defined keypoints per pig instance annotated in COCO keypoint format. Three floor types are represented: cast iron slatted, concrete, and plastic slatted. The dataset includes a video-level stratified train/test split (68 train videos / 3,099 frames; 17 test videos / 679 frames) designed to prevent data leakage from near-duplicate frames. Five pose estimation architectures are benchmarked: SLEAP UNet (AP 0.951), ResNet-50 (AP 0.955), HRNet-W32 (AP 0.958), RTMPose-m (AP 0.935), and ViTPose-S (AP 0.960). Full documentation, keypoint schema, and benchmark results are provided in README.md.猪姿态数据集（SwinePose）是首个面向无标记猪姿态估计与步态分析的公开侧视基准数据集。该数据集从3个商业化养猪场（Durofarm1、Sunds1、Ikadan1）录制的85段视频片段中提取了3778张标注帧，每张猪实例均按照COCO关键点标注格式标注15个解剖学定义的关键点。数据集覆盖三种地板类型：铸铁条缝地板、混凝土地板与塑料条缝地板。该数据集采用视频层级的分层训练/测试划分方案（68段训练视频，合计3099帧；17段测试视频，合计679帧），可有效避免近重复帧引发的数据泄露问题。本次基准测试涵盖5种姿态估计模型：SLEAP UNet（AP 0.951）、ResNet-50（AP 0.955）、HRNet-W32（AP 0.958）、RTMPose-m（AP 0.935）以及ViTPose-S（AP 0.960）。完整的文档说明、关键点标注规范与基准测试结果均已在README.md中提供。