Detecting Crowd Features in Video Sequences

Favaretto, Rodolfo Migon; Musse, Soraia Raupp

doi:10.1109/sibgrapi.2016.036

Cited by 19 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To compute the collectivity affecting one individual i from all n i individuals in his/her social space (as presented in [38]), we used Equation 1:…”

Section: A Individuals Data Extractionmentioning

confidence: 99%

Using Big Five Personality Model to Detect Cultural Aspects in Crowds

Favaretto

Musse

Vilanova

et al. 2017

2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…To compute the collectivity affecting one individual i from all n i individuals in his/her social space (as presented in [38]), we used Equation 1:…”

Section: A Individuals Data Extractionmentioning

confidence: 99%

Using Big Five Personality Model to Detect Cultural Aspects in Crowds

Favaretto

Musse

Vilanova

et al. 2017

2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once we get the socialization level ϑ i , we compute the isolation level ϕ i = 1 − ϑ i , that corresponds to its inverse. For more details about how this features are obtained, please refer to [24], [25]. For each individual i in a video, we computed the average for all frames and generate a vector V i of extracted data where…”

Section: First and Second Dimensions: Data Extraction Crowd Typmentioning

confidence: 99%

“…After this step, a series of information about the permanent groups are computed, such as the number G f of groups, mean speeds g (in meters/second) among all the members of g, mean angular variationᾱ g (in degrees) among all the members of g, group area A g and group cohesion C g . All these information are described in [25].…”

Section: First and Second Dimensions: Data Extraction Crowd Typmentioning

confidence: 99%

Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind

Favaretto¹

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

View full text Add to dashboard Cite

In this work we proposed a computational model to extract pedestrian characteristics from video sequences. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. As one of the main contributions, four large dimensions of geometric characteristics (Big4GD) were proposed: I - Physical, II - Personal and Emotional, III - Social and IV - Cultural, which seek to describe the behavior of pedestrians and groups in the crowd. The GeoMind tool was developed for the purpose of detecting the four geometric dimensions from video sequences. In addition, several analyzes were carried out with the purpose of validating the proposed model, from comparing results with the literature, including the comparison of spontaneous multitudes from several countries and controlled experiments involving Fundamental Diagrams.

show abstract

“…Typically, these approaches deal with Natural Language Processing (NLP) and extractions of social media data (analysis of feelings), criminal and medical records, or any other record extracted from textual data. cultural aspects using controlled experiment videos (related to Fundamental Diagram [11]) and spontaneous videos from various countries, using geometrical features [12], Big-Five personality [13] and OCC emotion [14] models. However, there are not many methods in the literature that investigate people's perceptions regarding geometric information [3].…”

Section: Introductionmentioning

confidence: 99%

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Araujo

Favaretto

Knob

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Detecting Crowd Features in Video Sequences

Cited by 19 publications

References 28 publications

Using Big Five Personality Model to Detect Cultural Aspects in Crowds

Using Big Five Personality Model to Detect Cultural Aspects in Crowds

Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind

How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)

Contact Info

Product

Resources

About