Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

Favaretto, Rodolfo Migon; Santos, Roberto R.; Balotin, Marcio; Knob, Paulo; Musse, Soraia Raupp; Vilanova, Felipe; Costa, Ângelo Brandelli

doi:10.1002/cav.1899

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…A culture factor is one of the factors that influence the fundamental diagrams (FDs) [ [3] , [4] , [5] ]. However, the study also wants to consider geographical location in this experiment.…”

Section: Location/cultural Differences In Speed–density Relationmentioning

confidence: 99%

“…Researchers of pedestrian behaviour and crowd modelling have recently taken notice of the importance of studying and modelling social group behaviour [ [1] , [2] , [3] , [4] , [5] ]. It is critical to understand and model groups to recreate crowd behaviour during normal and emergencies since groups make up a significant proportion or even the majority of urban pedestrian crowds [ 6 , 7 ] and exhibit group-specific behaviours [ [8] , [9] , [10] ].…”

Section: Introductionmentioning

confidence: 99%

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Experimental study of the effects of culture and location on single-file fundamental diagrams

Bilintoh,

Zhang,

Karim

et al. 2023

Heliyon

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Section: Location/cultural Differences In Speed–density Relationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental study of the effects of culture and location on single-file fundamental diagrams

Bilintoh,

Zhang,

Karim

et al. 2023

Heliyon

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“…Some recent work presents approaches in crowd level [11,12,37]. In [11], the authors propose a light-weight and fast fully-convolutional neural network to learn a regression model for crowd counting in images acquired from drones.…”

Section: Previous Approaches In Cultural Behaviorsmentioning

confidence: 99%

“…[12] introduces a crowd detection method for drone safe landing. In the work presented in [37], the authors proposed an approach to detect cultural aspects from crowds using Convolutional Neural Networks (CNN).…”

Section: Previous Approaches In Cultural Behaviorsmentioning

confidence: 99%

Cultural behaviors analysis in video sequences

Favaretto

Araujo

Vilanova

et al. 2021

Machine Vision and Applications

Self Cite

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In this paper, we investigate the cultural aspects of different populations from video sequences. For that, we proposed a model that considers a series of characteristics of the pedestrians and the crowd, such as distances and speeds and performs the mapping of these characteristics in personalities, emotions, and cultural aspects. The model called Big4GD consists of four dimensions of geometric characteristics and seeks to describe the behavior of pedestrians and groups in the crowd. We performed a study of group behavior in a controlled experiment and focused on differences in two attributes that vary across cultures: (walking speed and personal distance) in three countries (India, Brazil, and Germany). We use the Fundamental Diagram theory that determines the relationship between the density and speed of individuals. We use Computer Vision methods to detect and track individuals through video sequences by generating their positions and speeds as a function of time. With these data, we analyze emergent walking speeds and densities while considering the personal distance of each individual and the neighbor in front of him/her. Our results show that human behavior is more similar in highly dense populations, i.e., individuals behave like a mass when presented with limited free personal space. The opposite result is also relevant: cultural differences can be observed at low and moderate densities, and such assumptions can be applied to computational interfaces and simulations, games, and movies. Besides, we present GeoMind, a software we developed to detect a series of characteristics from pedestrians. We also performed a practical case-study using GeoMind focusing on event detection in video sequences.

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“…On this subject, Chattaraj et al [8] suggested that cultural and population differences could produce deviations in speed, density and flow of the crowd. In [29], the authors presents a study regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures -the personal spaces, this study was conducted in two countries: Brazil and Germany. Cultural aspects are specific to a group of individuals.…”

Section: Introductionmentioning

confidence: 99%

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

Favaretto,

Araujo,

Musse

et al. 2019

Preprint

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This paper presents a video analysis application to detect personality, emotion and cultural aspects from pedestrians in video sequences, along with a visualizer of features. 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. The software was tested in a set of videos from different countries and results seem promising in order to identify these three different levels of psychological traits in the filmed sequences. In addition, the data of the people present in the videos can be seen in a crowd viewer.

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Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation

Cited by 5 publications

References 33 publications

Experimental study of the effects of culture and location on single-file fundamental diagrams

Experimental study of the effects of culture and location on single-file fundamental diagrams

Cultural behaviors analysis in video sequences

A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences

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