Extracting Accurate Long-Term Behavior Changes from a Large Pig Dataset

Luca Bergamini, Stefano Pini, Alessandro Simoni, Roberto Vezzani, Simone Calderara, Rick B. D’Eath, Robert B Fisher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Visual observation of uncontrolled real-world behavior leads to noisy observations, complicated by occlusions, ambiguity, variable motion rates, detection and tracking errors, slow transitions between behaviors, etc. We show in this paper that reliable estimates of long-term trends can be extracted given enough data, even though estimates from individual frames may be noisy. We validate this concept using a new public dataset of approximately 20+ million daytime pig observations over 6 weeks of their main growth stage, and we provide annotations for various tasks including 5 individual behaviors. Our pipeline chains detection, tracking and behavior classification combining deep and shallow computer vision techniques. While individual detections may be noisy, we show that long-term behavior changes can still be extracted reliably, and we validate these results qualitatively on the full dataset. Eventually, starting from raw RGB video data we are able to both tell what pigs main daily activities are, and how these change through time.
Original languageEnglish
Title of host publicationProceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 5)
Pages524 - 533
Number of pages10
ISBN (Electronic)9789897584886
Publication statusPublished - 8 Feb 2021
Event16th International Conference on Computer Vision Theory and Applications - Online Conference
Duration: 8 Feb 202110 Feb 2021

Publication series

ISSN (Electronic)2184-4231


Conference16th International Conference on Computer Vision Theory and Applications
Abbreviated titleVISAPP 2021
CityOnline Conference
Internet address

Keywords / Materials (for Non-textual outputs)

  • pig detection
  • pig tracking
  • behavior classification
  • pig farming
  • long-term temporal analysis


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