Detecting Personality and Emotion Traits in Crowds from Video Sequences

TL;DR

This paper introduces a method to analyze crowds in videos to detect personality and emotion traits by tracking individuals, recognizing groups, and mapping behaviors to OCEAN personality dimensions, considering cultural differences.

Contribution

It presents a novel approach combining crowd tracking, group recognition, and personality/emotion inference from video data using OCC models, validated against literature data.

Findings

The model produces coherent personality and emotion profiles consistent with literature.

Results reflect cultural differences in personality and personal space.

Qualitative and quantitative evaluations support the method's validity.

Abstract

This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, then groups are recognized and characterized. Such information is then mapped to OCEAN dimensions, used to find out personality and emotion in videos, based on OCC emotion models. Although it is a clear challenge to validate our results with real life experiments, we evaluate our method with the available literature information regarding OCEAN values of different Countries and also emergent Personal distance among people. Hence, such analysis refer to cultural differences of each country too. Our results indicate that this model generates coherent information when compared to data provided in available literature, as shown in qualitative and quantitative results.