Emotion estimation in crowds: a survey

Urizar, Oscar J.; Barakova, Emilia; Regazzoni, Carlo S.; Rauterberg, Matthias

doi:10.1049/cp.2017.0147

Cited by 9 publications

(2 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the latter two features, namely valence and arousal, are related to emotional aspects [26] . Crowd emotions occur when the same event yields a similar appraisal and elicits a common emotion among the members of a crowd [27] . Crowds suffer from strong emotion regulation and contagion processes [28] , which are critical to monitor.…”

Section: A Taxonomy For Crowd Behaviour Analysismentioning

confidence: 99%

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Sánchez

Hupont²,

Tabik

et al. 2020

Information Fusion

View full text Add to dashboard Cite

Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed.

show abstract

Section: A Taxonomy For Crowd Behaviour Analysismentioning

confidence: 99%

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Sánchez

Hupont²,

Tabik

et al. 2020

Information Fusion

View full text Add to dashboard Cite

show abstract

“…The topics discussed are: group types and emotion models considered in the literature, datasets, general approaches taken and reported performances of the proposed methods. A survey paper by [17] discusses some core methods and datasets used for emotion estimation in crowds before 2017. Since then, the task of group emotion recognition has evolved into a broad research area, concerning many different crowds, emotions, techniques, and approaches.…”

Section: Introductionmentioning

confidence: 99%

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

show abstract

A Deep Learning Approach to Predict Crowd Behavior Based on Emotion

Varghese

Thampi

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Emotion estimation in crowds: a survey

Cited by 9 publications

References 46 publications

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

Automatic Emotion Recognition for Groups: A Review

A Deep Learning Approach to Predict Crowd Behavior Based on Emotion

Contact Info

Product

Resources

About