The use of information technology in the study of human behavior is a subject of great scientific interest. Cultural and personality aspects are factors that influence how people interact with one another in a crowd. This paper presents a methodology to detect cultural characteristics of crowds in video sequences. Based on filmed sequences, pedestrians are detected, tracked and characterized. Such information is then used to find out cultural differences in those videos, based on the Big-five personality model. Regarding cultural differences of each country, results indicate that this model generates coherent information when compared to data provided in literature.

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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. Keywords Computer vision • crowd features • Big-five model • cultural dimensions • crowd emotion Thanks to Office of Naval Research Global (USA) and Brazilian agencies: CAPES, CNPQ and FAPERGS.

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This paper presents a study, organized in two phases, regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures—personal spaces. First, we want to study and compare the spatial behavior different populations adopt with respect to their personal space. Second, we want to use simulation of virtual agents to artificially generate movements of people in similar situations and validate them using real video sequences. Our main goal is to be able to extract from video sequences and then simulate variations in populations in a coherent way with literature that studies cultural aspects. In addition to the cultural aspects, we also investigate the personality model in the studied videos using OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Finally, we propose a way to simulate the fundamental diagram experiment from other countries using the OCEAN psychological trait model as input. Results indicate that the simulated countries have consistent characteristics with the expected literature.

This work aims to evaluate people's perception regarding geometric features, personalities and emotions characteristics in virtual humans. For this, we use as a basis, a dataset containing the tracking files of pedestrians captured from spontaneous videos and visualized them as identical virtual humans. The goal is to focus on their behavior and not being distracted by other features. In addition to tracking files containing their positions, the dataset also contains pedestrian emotions and personalities detected using Computer Vision and Pattern Recognition techniques. We proceed with our analysis in order to answer the question if subjects can perceive geometric features as distances/speeds as well as emotions and personalities in video sequences when pedestrians are represented by virtual humans. Regarding the participants, an amount of 73 people volunteered for the experiment. The analysis was divided in two parts: i) evaluation on perception of geometric characteristics, such as density, angular variation, distances and speeds, and ii) evaluation on personality and emotion perceptions. Results indicate that, even without explaining to the participants the concepts of each personality or emotion and how they were calculated (considering geometric characteristics), in most of the cases, participants perceived the personality and emotion expressed by the virtual agents, in accordance with the available ground truth.