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

Centorrino, Pietro; Corbetta, Alessandro; Cristiani, Emiliano; Onofri, Elia

doi:10.1016/j.jocs.2021.101357

Cited by 51 publications

(34 citation statements)

References 49 publications

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“…In this section, we discuss the basics of RSSi-based tracking considering our measurement installation at the Galleria Borghese (GB) museum, Rome, Italy, which we thoroughly described in our previous work [4]. GB hosts hundreds of art pieces in a relatively small area, and consists of 21 exhibition volumes divided into two floors, which are accessible via 3 entrances.…”

Section: Rssi-based Visitor Tracking At Galleria Borghesementioning

confidence: 99%

“…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%

“…These constraints likewise jeopardise the success of any approach based on instantaneously "maximum RSSi" readings (argMax), even when the ambition is the sole room-level localisation. To deal with these limitations, we proposed in [4] a method based on an end-to-end Multi-Layer Perception (MLP) neural network. This MLP significantly improved room-level localisation performances over naïve argMax policies by considering short RSSi time sequences.…”

Section: Introductionmentioning

confidence: 99%

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

Onofri

Corbetta

2022

Coll Dyn

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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: Rssi-based Visitor Tracking At Galleria Borghesementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Onofri

Corbetta

2022

Coll Dyn

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“…Distance-to-object data can also be used to analyze typical user behavior and activity profiles. This type of problem was addressed in the paper 24 , in which groups of visitors representing specific individual as well as social behavior profiles were extracted. BLE beacons were used, deployed both in the museum halls and provided to the visitors.…”

Section: Related Workmentioning

confidence: 99%

Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

Filus

Nowak

Domańska

et al. 2021

Preprint

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Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity of a moving object with respect to static points (anchors), evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one-Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

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“…Highly accurate, optical methods are generally limited in range by the visual cone of the individual sensors. Note that, at the price of a substantial accuracy loss, Bluetoothbased [18,19] or Wi-Fi-based [20,21] tracking enable larger spatial coverage per sensor.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

et al. 2022

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High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research allowing to quantify statistics of relevant observables including walking velocities, mutual distances and body orientations. As this technology advances, it is becoming increasingly useful also in society. In fact, continued urbanization is overwhelming existing pedestrian infrastructures such as transportation hubs and stations, generating an urgent need for real-time highly-accurate usage data, aiming both at flow monitoring and dynamics understanding. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. This is not only necessary to guarantee data quality, but also to identify systematic errors. Currently, there is no established policy in this context. In this contribution, we present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques. The suite is technology-independent and it is applicable to academic and commercial pedestrian tracking systems, operating both in lab environments and real-life conditions. The benchmark suite consists of 5 tests addressing specific aspects of pedestrian tracking quality, including accurate line-based crowd flux estimation, local density estimation, individual position detection and trajectory accuracy. The output of the tests are quality factors expressed as single numbers. We provide the benchmark results for two tracking systems, both operating in real-life, one commercial, and the other based on overhead depth-maps developed at TU Eindhoven, within the Crowdflow topical group. We discuss the results on the basis of the quality factors and report on the typical sensor and algorithmic performance. This enables us to highlight the current state-of-the-art, its limitations and provide installation recommendations, with specific attention to multi-sensor setups and data stitching.

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

Cited by 51 publications

References 49 publications

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

Cost-Effective Proximity Detection and Filtering of Unreliable Results Based on BLE RSSI and IMU Readings Using Smartphones for Indoor Environments

Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

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