CREMD: Crowd-Sourced Emotional Multimodal Dogs Dataset

TL;DR

This paper introduces CREMD, a new multimodal dataset for dog emotion recognition, analyzing how context, audio, and annotator background affect labeling accuracy and confidence.

Contribution

CREMD is the first comprehensive dataset exploring multimodal cues and annotator factors influencing dog emotion perception, with detailed analysis of annotation agreement and confidence.

Findings

Visual context improves annotation agreement.

Audio cues increase annotator confidence for certain emotions.

Professionals and non-owners show higher agreement levels.

Abstract

Dog emotion recognition plays a crucial role in enhancing human-animal interactions, veterinary care, and the development of automated systems for monitoring canine well-being. However, accurately interpreting dog emotions is challenging due to the subjective nature of emotional assessments and the absence of standardized ground truth methods. We present the CREMD (Crowd-sourced Emotional Multimodal Dogs Dataset), a comprehensive dataset exploring how different presentation modes (e.g., context, audio, video) and annotator characteristics (e.g., dog ownership, gender, professional experience) influence the perception and labeling of dog emotions. The dataset consists of 923 video clips presented in three distinct modes: without context or audio, with context but no audio, and with both context and audio. We analyze annotations from diverse participants, including dog owners, professionals, and individuals with varying demographic backgrounds and experience levels, to identify factors that influence reliable dog emotion recognition. Our findings reveal several key insights: (1) while adding visual context significantly improved annotation agreement, our findings regarding audio cues are inconclusive due to design limitations (specifically, the absence of a no-context-with-audio condition and limited clean audio availability); (2) contrary to expectations, non-owners and male annotators showed higher agreement levels than dog owners and female annotators, respectively, while professionals showed higher agreement levels, aligned with our initial hypothesis; and (3) the presence of audio substantially increased annotators' confidence in identifying specific emotions, particularly anger and fear.