用视频分析自动识别禽类玩耍行为，提升动物福利监测。
PlayClass: Automated Play Behaviour Classification in Poultry
- 结合YOLO分段与SAM3跟踪，减少身份混淆。
- 融合手工特征与V-JEPA2.1模型，达77.0%宏F1分数。
- 适合关注动物行为分析与智能养殖的研究者。
自动化动物福利监测多聚焦负面行为，对玩耍等积极行为关注不足。为此，我们提出PlayClass，一个从顶部视角鸡舍视频中分类玩耍行为的流程。该流程利用YOLO引导的分块边界进行长时跟踪，结合SAM3减少基于点的提示误差；并采用冻结的图像与视频基础模型嵌入进行动作分类。仅使用追踪掩码的手工运动特征已具竞争力，而V-JEPA 2.1在所有模型尺度上表现最优，与手工特征结合后达到77.0%的宏平均F₁分数。尽管如此，数据集仍具挑战性，因玩耍子类型与非玩耍行为具有相似运动模式，且存在鸟间遮挡。本研究为禽类玩耍行为自动化分类提供了有力证据。
原文摘要 · Abstract (English)
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.
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