PlayClass: Automated Play Behaviour Classification in Poultry

Prince Ravi Leow; Neil Scheidwasser; Rebecca Oscarsson; Per Jensen; Samir Bhatt; David Alejandro Duchêne

cs.CVMay 26, 2026

PlayClass: Automated Play Behaviour Classification in Poultry

Authors: Prince Ravi Leow, Neil Scheidwasser, Rebecca Oscarsson, Per Jensen, Samir Bhatt, David Alejandro Duchêne

Organizations: Section for Health Data Science & AI, University of Copenhagen · 1Section for Health Data Science & AI, University of Copenhagen · Department of Infectious Disease Epidemiology, Imperial College London · AVIAN Behaviour Genomics and Physiology Group, Linköping University · 2AVIAN Behaviour Genomics and Physiology Group, Linköping University

Abstract

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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Aug 5, 2026 · cs.CVJ/K move · Enter open · S save

Promptable Animal Pose Tracking Across Species

Le Li, Daniela Ivanova, Nicolas Pugeault

School of Computing Science University of Glasgow

Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative. While human pose estimation and tracking has seen rapid progress thanks to large annotated datasets, animal pose remain challenging, due to large morphological and behavioural differences between species and limited annotated data. Existing approaches either optimise generic keypoint localisation from annotated datasets (such as APTv2) with poor generalisation, or track custom keypoints using visual tracking, at the cost of performance. In this paper, we demonstrate that vision foundation models trained on large datasets can be used effectively to track animal pose with limited labelled data. We propose two models, one unsupervised and the other supervised, to track user-selected keypoints in videos. The supervised approach delivers superior tracking accuracy by employing a keypoint prompt encoder to explicitly inject structural priors from a reference frame into feature matching. In parallel, the unsupervised route provides strong cross-species robustness by leveraging diverse foundation-model features for training-free correspondence matching. Extensive evaluation on challenging animal video benchmarks APTv2 and TigDog demonstrates that our framework achieves strong performance while maintaining an effective balance between accuracy and generalisation, offering a practical solution for real-world animal behaviour analysis and conservation applications.

Keypoint Detection

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Abstract

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