Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut

Kim Ferres, Timo Schloesser and Peter A. Gloor
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Kim Ferres: Department of Information Systems and Information Management, University of Cologne, Pohligstrasse 1, 50969 Cologne, Germany
Timo Schloesser: Department of Information Systems and Information Management, University of Cologne, Pohligstrasse 1, 50969 Cologne, Germany
Peter A. Gloor: MIT Center for Collective Intelligence, 245 First Street, Cambridge, MA 02142, USA

Future Internet, 2022, vol. 14, issue 4, 1-16

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.

Keywords: neural networks; machine learning; dog emotion detection; dog emotion (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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