Research library

SLEAP: A deep learning system for multi-animal pose tracking

Pereira 1,8, Nathaniel Tabris 1, Arie Matsliah1, David M. Turner1, Junyu Li1, Shruthi Ravindranath1, Eleni S. Mitelut6, Marielisa Diez Castro6, John D’Uva1,9, Mikhail Kislin 1, Dan H. Shaevitz 1,4,5,10 and Mala Murthy 1,10 ✉ The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior.

Publication details

Authors
Talmo D. Pereira
Organizations
🇺🇸 Princeton University🇺🇸 New York University🇺🇸 Johns Hopkins University School of Medicine
Year
2022
Type
Journal

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs entrance and behavior analytics in the Gratheon web app, camera-based hive-scanner and computer-vision models. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.