文章基本信息

标题：Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut
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作者：Kim Ferres ; Timo Schloesser ; Peter A. Gloor 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2022
卷号：14
期号：4
页码：97
DOI：10.3390/fi14040097
语种：English
出版社：MDPI Publishing
摘要：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.