Forecasting Visitors’ behaviour in Crowded Museums

Balzotti, Caterina; Briani, Maya; Corbetta, Alessandro; Cristiani, Emiliano; Minozzi, Marina; Natalini, Roberto; Suriano, Sara; Toschi, Federico

doi:10.17815/cd.2020.82

Cited by 5 publications

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“…For instance, understanding visitors' dynamics unlocks focused tuning of visiting paths, collocation of pieces, and access/ticketing strategies. The recording and analysis of individual visitors' trajectories -possibly across the entire museum venue -is a great asset towards such behavioural analyses [4], allowing even to regenerate plausible visiting patterns [5,6]. Their feasibility has significantly grown during the last decades thanks, particularly, to the diffusion of Internet-of-Things (IoT) technologies [7][8][9], which enabled individual tracking needlessly of invasive structural modifications (e.g.…”

Section: Introductionmentioning

confidence: 99%

RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

Onofri

Corbetta

2022

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Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural analyses and comfort/performance improvements, is seeing increasing diffusion. Conceptually, this approach enables room-level localisation based on a network of small antennas (thus, without invasive modification of the existent structures). The antennas measure the intensity (RSSi) of self-advertising signals broadcasted by beacons individually assigned to the visitors. The signal intensity provides a proxy for the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd, ...). In this contribution, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages >96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).

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Section: Introductionmentioning

confidence: 99%

RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

Onofri

Corbetta

2022

Coll Dyn

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“…For instance, understanding visitors' dynamics unlocks focused tuning of visiting paths, collocation of pieces, and access/ticketing strategies. The recording and analysis of individual visitors' trajectories -possibly across the entire museum venue -is a great asset towards such behavioural analyses [6], allowing even to re-generate plausible visiting patterns [2,19]. Their feasibility has significantly grown during the last decades thanks, particularly, to the diffusion of Internet-of-Things (IoT) technologies [11,25,7], which enabled individual tracking needlessly of invasive structural modifications (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…A partition of a set V is a set of disjoint subsets K 1 , K 2 , • • • ⊆ V such that their union gives the entire set 2. As an additional feature, it can be useful to reduce the cost of the first door transition, actually decreasing the weight of short transitions that can occur if visitors stand still by the entrance door.…”

mentioning

confidence: 99%

RSSi-based visitor tracking in museums via cascaded AI classifiers and coloured graph representations

Onofri,

Corbetta

2021

Preprint

Self Cite

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Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural analyses and comfort/performance improvements, is seeing increasing diffusion. Conceptually, this approach enables room-level localisation based on a network of small antennas (thus, without invasive modification of the existent structures). The antennas measure the intensity (RSSi) of self-advertising signals broadcasted by beacons individually assigned to the visitors. The signal intensity provides a proxy for the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd, . . . ).In this contribution, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages > 96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).

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“…Museums digital twin: once statistics about visitors trajectories and behavior are available, it is possible to create an algorithm capable of generating real-like visits paths in the museum [1]. This is done by reproducing the movements of people from one room to another, duly determining their transition probability.…”

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confidence: 99%

“…Unfortunately, that simulator can be hardly used in a museum with a very high density of artworks exposed, since it requires a complex calibration of many artwork-scale parameters which usually show a high variance between visitors. See also [1] for a rudimentary simulator on graph and [42] for a simulator developed under the NetLogo software environment.…”

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confidence: 99%

Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization

Centorrino

Corbetta

Cristiani

et al. 2021

Journal of Computational Science

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Forecasting Visitors’ behaviour in Crowded Museums

Cited by 5 publications

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RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations

RSSi-based visitor tracking in museums via cascaded AI classifiers and coloured graph representations

Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization

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