Automated monitoring of animal welfare has largely targeted negative indicators, leaving positive welfare behaviours such as play underexplored. To address this gap, we present PlayClass, a pipeline for play-behaviour classification in poultry from top-down pen video. The pipeline leverages long-duration tracking with SAM 3 via YOLO-guided chunk boundaries to minimise identity errors in point-based prompting, and frozen embeddings from image and video foundation models for play action classification. Although handcrafted motion features from tracked masks alone achieved competitive accuracy, V-JEPA 2.1 consistently outperformed all other backbones across model scales, reaching 77.0 macro-averaged F$_1$ when combined with handcrafted features. Despite this result, the dataset remains challenging due to play sub-types sharing similar kinematic profiles with non-play and inter-bird occlusion. Overall, our work provides encouraging evidence towards automated frameworks for play behaviour classification in poultry.

翻译：自动化动物福利监测主要聚焦于负面指标，而玩耍等积极福利行为尚未得到充分探索。为弥补这一空白，我们提出PlayClass——一种基于俯拍栏舍视频对家禽玩耍行为进行分类的流水线。该流水线利用SAM 3进行长时间跟踪，并通过YOLO引导的片段边界最小化点提示中的身份误差，同时采用图像与视频基础模型的冻结嵌入进行玩耍动作分类。尽管仅基于跟踪掩模的手工运动特征已取得具有竞争力的准确率，但V-JEPA 2.1在不同模型规模下均持续优于其他主干网络，结合手工特征后宏平均F₁达到77.0。然而，由于不同玩耍子类型与非玩耍行为具有相似的运动学特征，且存在鸟类间相互遮挡，该数据集仍具挑战性。总体而言，本研究为家禽玩耍行为自动分类框架提供了积极证据。