Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut
Name
futureinternet-14-00097.pdf
Size
2.13 MB
Format
Adobe PDF
Checksum (MD5)
946e18695dc582cad5d4cc371ec2b70c
Author(s)
Ferres, Kim
Schloesser, Timo
Gloor, Peter A.
Date Issued
March 22, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Future Internet 14 (4): 97 (2022)
Version
Final published version
Abstract
This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly trained detector learned from a total of 13,809 annotated dog images and possesses the capability to estimate the coordinates of 24 different dog body part keypoints. Our application is able to determine a dog’s emotional state visually with an accuracy between 60% and 70%, exceeding human capability to recognize dog emotions.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/fi14040097
