You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: This study addresses the question of whether any facial expressions of emotion are universal. Recent studies showing that members of literate cultures associated the same emotion concepts with the same facial behaviors could not demonstrate that at least some facial expressions of emotion are universal; the cultures compared had all been exposed to some of the same mass media presentations of facial expression, and these may have taught the people in each culture to recognize the unique facial expressions of other cultures. To show that members of a preliterate culture who had minimal exposure to literate cultures would associate the same emotion concepts with the same facial behaviors as do members of Western and Eastern literate cultures, data were gathered in New Guinea by telling subjects a story, showing them a set of three faces, and asking them to select the face which showed the emotion appropriate to the story. The results provide evidence in support of the hypothesis that the association between particular facial muscular patterns and discrete emotions is universal.

[2]: A common phenomenon in everyday life is that, when a strange event occurs or is announced, a regular crowd can completely change, showing different intense emotions and sometimes uncontrollable and violent emerging behavior. These emotions and behaviors that disturb the organization of a crowd are of concern in our study, and we attempt to predict these suspicious circumstances and provide help in making the right decisions at the right time. Furthermore, most of the models that address crowd disasters belong to the physical or the cognitive approaches. They study pedestrian flow and collision avoidance, etc., and they use walking speed and angle of vision. However, in this work, based on a behavioral rules approach, we aim to model emergent emotion, behavior and influence in a crowd, taking into account particularly the personality of members of the crowd. For this purpose, we have combined the OCEAN Openness, Consciousness, Extraversion, Agreeableness, and Neuroticism personality model with the OCC Ortony, Clore, and Collins emotional model to indicate the susceptibility of each of the five personality factors to feeling every emotion. Then we proposed an approach that uses first fuzzy logic for the emotional modeling of critical emotions of members of the crowd at the announcement or the presence of unusual events, in order to quantify emotions. Then, we model the behavior and the tendency towards actions using probability theory. Finally, the influence among the members of the crowd is modeled using the neighborhood principle and cellular automata.

[3]: This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, and 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.
Several models have been developed to explain and quantify basic emotions in humans. One of the most cited is proposed by Paul Ekman [1] which considers the existence of 6 universal emotions based on cross-cultural facial expressions (anger, disgust, fear, happiness, sadness and surprise). [3] , the authors proposed a way to detect pedestrian emotions in videos, based on OCC emotion model. To detect the emotions of each pedestrian, the authors used OCEAN as inputs, as proposed by Saifi [2] . In our approach, we proceed with an analysis in order to verify 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. Next section present how we performed the analysis.