A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset

• URL: http://arxiv.org/abs/2509.12047v1
• Date: Mon, 15 Sep 2025 15:31:12 GMT
• Title: A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset
• Authors: Haiyu Yang, Enhong Liu, Jennifer Sun, Sumit Sharma, Meike van Leerdam, Sebastien Franceschini, Puchun Niu, Miel Hostens,
• Abstract summary: Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings.<n>We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment.<n>Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition.
• Score: 0.46297934208241753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with 93.3% identity preservation score and 89.3% object detection precision. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.

Related papers

• Cattle-CLIP: A Multimodal Framework for Cattle Behaviour Recognition [5.45546363077543]
  Cattle-CLIP is a multimodal deep learning framework for cattle behaviour recognition.<n>It is adapted from the large-scale image-language model CLIP by adding a temporal integration module.<n>Experiments show that Cattle-CLIP achieves 96.1% overall accuracy across six behaviours in a supervised setting.
  arXiv  Detail & Related papers  (2025-10-10T09:43:12Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• An Explainable AI based approach for Monitoring Animal Health [0.2749898166276853]
  Monitoring cattle health and optimizing yield are key challenges faced by dairy farmers due to difficulties in tracking all animals on the farm.<n>This work aims to showcase modern data-driven farming practices based on explainable machine learning(ML) methods that explain the activity and behaviour of dairy cattle (cows)
  arXiv  Detail & Related papers  (2025-08-13T21:40:35Z)
• Benchmarking pig detection and tracking under diverse and challenging conditions [1.865175170209582]
  We curated two datasets: PigDetect for object detection and PigTrack for multi-object tracking.<n>For object detection, we show that challenging training images improve detection beyond what is achievable with randomly sampled images alone.<n>For multi-object tracking, we observed that SORT-based methods achieve superior detection performance compared to end-to-end trainable models.
  arXiv  Detail & Related papers  (2025-07-22T14:36:51Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions [57.871692507044344]
  Pose estimation aims to accurately identify anatomical keypoints in humans and animals using monocular images. Current models are typically trained and tested on clean data, potentially overlooking the corruption during real-world deployment. We introduce PoseBench, a benchmark designed to evaluate the robustness of pose estimation models against real-world corruption.
  arXiv  Detail & Related papers  (2024-06-20T14:40:17Z)
• Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey [3.3651853492305177]
  This study presents the first systematic survey of publicly available livestock CV datasets. Among 58 public datasets identified and analyzed, almost half of them are for cattle, followed by swine, poultry, and other animals. Individual animal detection and color imaging are the dominant application and imaging modality for livestock.
  arXiv  Detail & Related papers  (2024-06-15T13:22:41Z)
• AnimalFormer: Multimodal Vision Framework for Behavior-based Precision Livestock Farming [0.0]
  We introduce a multimodal vision framework for precision livestock farming. We harness the power of GroundingDINO, HQSAM, and ViTPose models. This suite enables comprehensive behavioral analytics from video data without invasive animal tagging.
  arXiv  Detail & Related papers  (2024-06-14T04:42:44Z)
• TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos [63.85815474157357]
  We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
  arXiv  Detail & Related papers  (2022-11-17T23:55:12Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild [51.35013619649463]
  We present an extensive dataset of free-running cheetahs in the wild, called AcinoSet. The dataset contains 119,490 frames of multi-view synchronized high-speed video footage, camera calibration files and 7,588 human-annotated frames. The resulting 3D trajectories, human-checked 3D ground truth, and an interactive tool to inspect the data is also provided.
  arXiv  Detail & Related papers  (2021-03-24T15:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.