Estimating social relation from trajectories

Yücel, Zeynep; Zanlungo, Francesco; Feliciani, Claudio; Gregorj, Adrien; Kanda, Takayuki

doi:10.17815/cd.2020.54

Cited by 5 publications

(6 citation statements)

References 19 publications

(35 reference statements)

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“…This paper extends our preliminary work, which discriminated two kinds of social relations [3], by covering potentially the entire range of relation categories that occur between moving pedestrian dyads. In this respect, to the best of our knowledge, this work stands out as the first attempt to apply automatic social relation recognition in mobile settings.…”

Section: Introductionmentioning

confidence: 73%

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Identification of social relation within pedestrian dyads

Yücel

Zanlungo²,

Feliciani

et al. 2019

PLoS ONE

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This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories.

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Section: Introductionmentioning

confidence: 73%

“…age, gender, relation) and states (e.g. engagement in interaction) [1–3, 19]. Based on the results of [7, 17, 18], our work in [1] performs an analysis of four variables (namely, the dependence of group velocity, distance, abreast distance and distance in the direction of motion) for groups of different “intrinsic properties”.…”

Section: Introductionmentioning

confidence: 99%

Identification of social relation within pedestrian dyads

Yücel

Zanlungo²,

Feliciani

et al. 2019

PLoS ONE

Self Cite

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“…Interestingly, these effects on the spatial distribution of pedestrians do not lead to substantial changes in the flow-density relationship, suggesting that they are either not sufficiently important for the movement dynamics or that they are counter-balanced by other factors, related to walking speeds and overtaking, for example. It may be that under normal conditions social group effects are noticeable at a microscopic level (this is intuitive, as social groups can often be detected by observation [24]), but not at the macroscopic level.…”

Section: Discussionmentioning

confidence: 99%

“…Other researchers find that the positive or negative effect of social groups on pedestrian dynamics depends on the degree of familiarity between social groups [22] or the visibility conditions of the environment [23]. At the microscopic level previous work has investigated more detailed behaviours of social groups, such as the movement speed [15], movement path [24], response (decision) time [25], and relative positions [24] of group members. The desire of individuals in social groups to maintain group cohesion and to walk close to each other appears to result in differences between social groups and individuals.…”

Section: Introductionmentioning

confidence: 99%

Social groups barely change the speed-density relationship in unidirectional pedestrian flow, but affect operational behaviours

Zhang

Song

et al. 2021

Safety Science

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“…The interaction among pedestrians was studied in [20] by identifying groups and estimating the intensity of the interactions between people. In [19], a method was proposed for identifying the social relations between pedestrians in a group based on their motion trajectories and velocity, as well as the height differences between group members. These studies placed greater focus on the mechanics of the individuals and groups, which was reflected in their trajectories, but not on the effect they produced on the entire area.…”

Section: Related Workmentioning

confidence: 99%

Measuring “Nigiwai” From Pedestrian Movement

et al. 2021

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The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call ''Nigiwai,'' from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.

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Estimating social relation from trajectories

Cited by 5 publications

References 19 publications

Identification of social relation within pedestrian dyads

Identification of social relation within pedestrian dyads

Social groups barely change the speed-density relationship in unidirectional pedestrian flow, but affect operational behaviours

Measuring “Nigiwai” From Pedestrian Movement

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