# Detecting Personality and Emotion Traits in Crowds from Video Sequences

arXiv: https://arxiv.org/abs/2104.12927 · HTML: https://mlsanity.com/paper/2104.12927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Emotion-Based Crowd Representation for Abnormality Detection](https://mlsanity.com/paper/1607.07646/) (2016)
- [Using Big Five Personality Model to Detect Cultural Aspects in Crowds](https://mlsanity.com/paper/1903.01688/) (2019)

## Built on by (most cited first, 2 of 2)

- [Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey](https://mlsanity.com/paper/2207.10574/) (2022)
- [Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations](https://mlsanity.com/paper/2112.06428/) (2021)

## Abstract (arXiv)

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.