Animal Behavior Analysis

Follow this topic Ask about this topic

The field uses video, audio, and wearable sensors to quantify individual actions and social behavior, supporting research in ecology, animal welfare, health, and neuroscience. Common approaches include pose estimation and multi-object tracking, temporal action recognition with CNNs and transformers, multimodal sensor fusion, and mechanistic or agent-based models of collective motion. Work spans controlled laboratory studies, livestock monitoring, and wildlife camera-trap and underwater recordings, with benchmarks increasingly testing transfer across species and settings. A central challenge is making systems reliable on long, ambiguous sequences and under real-world distribution shifts, where strong performance on curated data may not translate to dependable biological conclusions.

Latest papers

37 papers7 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

BehaviorVLM: Unified Finetuning-Free Behavioral Understanding with Vision-Language Reasoning
Mar 12, 2026Jingyang Ke, Weihan Li, Amartya Pradhan +2Animal Behavior Analysis VLM Reasoning
Georgia Institute of Technology Atlanta, GA, 30332 · Georgia Institute of Technology, Emory University Atlanta, GA, 30322
Video-based Locomotion Analysis for Fish Health Monitoring
Mar 5, 2026Timon Palm, Clemens Seibold, Anna Hilsmann +1Aquaculture Animal Behavior Analysis
Fraunhofer HHI, Berlin, Germany · Humboldt University of Berlin, Berlin, Germany
An Interpretable Data-Driven Model of the Flight Dynamics of Hawks
Feb 22, 2026Lydia France, Karl Lapo, J. Nathan KutzAnimal Behavior Analysis
Department of Biology, University of Oxford, Oxford, UK · The Alan Turing Institute, London, UK · Department of Cyrospheric and Atmospheric Sciences, University of Innsbruck, Innsbruck, Austria +1
Rodent-Bench
Feb 20, 2026Thomas Heap, Laurence Aitchison, Emma Cahill +1Animal Behavior Analysis Video-Language Model Evaluation
University of Bristol, Bristol, UK
SiLVi: Simple Interface for Labeling Video Interactions
Nov 5, 2025Ozan Kanbertay, Richard Vogg, Elif Karakoc +3Animal Behavior Analysis Human-in-the-Loop Annotation
Institute of Computer Science and Campus Institute Data Science, University of Göttingen · Behavioral Ecology & Sociobiology Unit, German Primate Center, Göttingen, Germany · Department of Sociobiology/Anthropology, University of Göttingen, Göttingen, Germany
Cattle-CLIP: A Multimodal Framework for Dairy Cattle Behaviour Recognition from Video
Oct 10, 2025Huimin Liu, Jing Gao, Daria Baran +3Animal Behavior Analysis Video-Language Models
Bristol Veterinary School, University of Bristol, Langford House, Dolberry, Churchill, Bristol, BS40 5DU, United Kingdom · School of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, United Kingdom
MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis
Mar 13, 2025Teng Xu, Taotao Zhou, Youjia Wang +15Animal Behavior Analysis
School of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China. · LumiAni Technology, 201210, Shanghai, China. · School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China. +2

Mar 12, 2026J/K move · Enter open · S save

BehaviorVLM: Unified Finetuning-Free Behavioral Understanding with Vision-Language Reasoning

Jingyang Ke, Weihan Li, Amartya Pradhan, Jeffrey Markowitz+1

Georgia Institute of Technology Atlanta, GA, 30332 · Georgia Institute of Technology, Emory University Atlanta, GA, 30322

Animal Behavior Analysis VLM Reasoning

Open paper AskPDF arXiv

Understanding freely moving animal behavior is central to neuroscience, where pose estimation and behavioral understanding form the foundation for linking neural activity to natural actions. Yet both tasks still depend heavily on human annotation or unstable unsupervised pipelines, limiting scalability and reproducibility. We present BehaviorVLM, a unified vision-language framework for pose estimation and behavioral understanding that requires no task-specific finetuning and minimal human labeling by guiding pretrained Vision-Language Models (VLMs) through detailed, explicit, and verifiable reasoning steps. For pose estimation, we leverage quantum-dot-grounded behavioral data and propose a multi-stage pipeline that integrates temporal, spatial, and cross-view reasoning. This design greatly reduces human annotation effort, exposes low-confidence labels through geometric checks such as reprojection error, and produces labels that can later be filtered, corrected, or used to fine-tune downstream pose models. For behavioral understanding, we propose a pipeline that integrates deep embedded clustering for over-segmented behavior discovery, VLM-based per-clip video captioning, and LLM-based reasoning to merge and semantically label behavioral segments. The behavioral pipeline can operate directly from visual information and does not require keypoints to segment behavior. Together, these components enable scalable, interpretable, and label-light analysis of multi-animal behavior.