Detecting Personality and Emotion Traits in Crowds from Video Sequences
This work addresses the challenge of understanding crowd behavior for applications in surveillance or social analysis, but it is incremental as it builds on existing OCEAN and OCC models without major methodological breakthroughs.
The paper tackles the problem of detecting personality and emotion traits in crowds from video sequences by mapping group characteristics to OCEAN dimensions and OCC emotion models, with results indicating coherent information compared to literature data as shown in qualitative and quantitative analysis.
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.