Papers by Alessandro Corbetta
Collective Dynamics
Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural ... more 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 g...
Routing choices of walking pedestrians in geometrically complex environments are regulated by the... more Routing choices of walking pedestrians in geometrically complex environments are regulated by the interplay of a multitude of factors such as local crowding, (estimated) time to destination, (perceived) comfort. As individual choices combine, macroscopic traffic flow patterns emerge. Understanding the physical mechanisms yielding macroscopic traffic distributions in environments with complex geometries is an outstanding scientific challenge, with implications in the design and management of crowded pedestrian facilities. In this work, we analyze, by means of extensive real-life pedestrian tracking data, unidirectional flow dynamics in an asymmetric setting, as a prototype for many common complex geometries. Our environment is composed of a main walkway and a slightly longer detour. Our measurements have been collected during a dedicated high-accuracy pedestrian tracking campaign held in Eindhoven (The Netherlands). We show that the dynamics can be quantitatively modeled by introduci...
Collective Dynamics, 2022
In this work we present a simple routing model capable of capturing pedestrians path choices in t... more In this work we present a simple routing model capable of capturing pedestrians path choices in the presence of a herding effect. The model is tested and validated against data from a large scale tracking campaign which we have conducted during the GLOW 2019 festival. The choice between alternative paths is modeled as an individual cost minimization procedure, with the cost function being associated to the (estimated) traveling time. In order to trigger herding effects the cost function is supplemented with a penalty term, modulated as a function of the fraction of pedestrians walking along each route. The model is shown to provide an accurate quantitative description of the decision process.
Traffic and Granular Flow '15, 2016
DOI to the publisher's website. • The final author version and the galley proof are versions of t... more DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:
The development of turbulence closure models, parametrizing the influence of small non-resolved s... more The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance. We present a closure, based on deep recurrent neural networks, that quantitatively reproduces, within statistical errors, Eulerian and Lagrangian structure functions and the intermittent statistics of the energy cascade, including those of subgrid fluxes. To achieve high-order statistical accuracy, and thus a stringent statistical test, we employ shell models of turbulence. Our results encourage the development of similar approaches for 3D Navier-Stokes turbulence. Turbulence is the chaotic and ubiquitous dynamics of fluids, almost unavoidable for high velocity flows. Key to a vast number of environmental and industrial flows [15], 3D turbulence is characterized by a nonlinear forward energy cascade from large scales, where energy is injected, to smaller scales, where it...
Moving Light: can light steer a crowd?
Collective Dynamics, 2022
High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundament... more 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-...
Journal of Computational Science, 2021
We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangi... more We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort-and safetydriven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign. We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitors paths to get behavioral insights, including the most common flow patterns. On these bases, we build the transition matrix describing, in probability, the room-scale visitor flows. Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in silico. We conclude by employing the simulator to enhance the museum fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management.
2019 IEEE International Conference on Image Processing (ICIP), 2019
Unsupervised object discovery in images involves uncovering recurring patterns that define object... more Unsupervised object discovery in images involves uncovering recurring patterns that define objects and discriminates them against the background. This is more challenging than image clustering as the size and the location of the objects are not known: this adds additional degrees of freedom and increases the problem complexity. In this work, we propose StampNet, a novel autoencoding neural network that localizes shapes (objects) over a simple background in images and categorizes them simultaneously. StampNet consists of a discrete latent space that is used to categorize objects and to determine the location of the objects. The object categories are formed during the training, resulting in the discovery of a fixed set of objects. We present a set of experiments that demonstrate that StampNet is able to localize and cluster multiple overlapping shapes with varying complexity including the digits from the MNIST dataset. We also present an application of StampNet in the localization of pedestrians in overhead depth-maps.
Employing partially overlapping overhead \kinectTMS sensors and automatic pedestrian tracking alg... more Employing partially overlapping overhead \kinectTMS sensors and automatic pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear section of the main walkway of Eindhoven train station on a 24/7 basis. Beside giving access to the train platforms (it passes underneath the railways), the walkway plays an important connection role in the city. Several crowding scenarios occur during the day, including high- and low-density dynamics in uni- and bi-directional regimes. In this paper we discuss our recording technique and we illustrate preliminary data analyses. Via fundamental diagrams-like representations we report pedestrian velocities and fluxes vs. pedestrian density. Considering the density range $0$ - $1.1\,$ped/m$^2$, we find that at densities lower than $0.8\,$ped/m$^2$ pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above $0.8\,$ped/m$^2$.
ArXiv, 2019
We tackle the issue of measuring and analyzing the visitors’ dynamics in crowded museums. We prop... more We tackle the issue of measuring and analyzing the visitors’ dynamics in crowded museums. We propose an IoT-based system – supported by artificial intelligence models – to reconstruct the visitors’ trajectories throughout the museum spaces. Thanks to this tool, we are able to gather wide ensembles of visitors’ trajectories, allowing useful insights for the facility management and the preservation of the art pieces. Our contribution comes with one successful use case: the Galleria Borghese in Rome, Italy.
In this thesis we investigate the dynamics of pedestrian crowds in a fundamental and applied pers... more In this thesis we investigate the dynamics of pedestrian crowds in a fundamental and applied perspective. Envisioning a quantitative understanding we employ ad hoc large-scale experimental measurements as well as analytic and numerical models. Moreover, we analyze current regulations in matter of pedestrians structural actions (structural loads), in view of the need of guaranteeing pedestrian safety in serviceable built environments. This work comes in three complementary parts, in which we adopt distinct perspectives and conceptually different tools, respectively from statistical physics, mathematical modeling and structural engineering. Chapter 1 introduces these perspectives and gives an outline of the thesis. The statistical dynamics of individual pedestrians is the subject of Part I. Although individual trajectories may appear random, once we analyze them in large ensembles we expect ``preferred'' behaviors to emerge. Thus, we envisage individual paths as fluctuations a...
Bulletin of the American Physical Society, 2017
Based on the observed statistics we drew an analogy between undisturbed pedestrians (i.e. the dil... more Based on the observed statistics we drew an analogy between undisturbed pedestrians (i.e. the diluted dynamics) and active Brownian particles and constructed a generalized Langevin model (1) that takes into account geometric restrictions, pedestrians' destination and turningback events [1,2].
Complexity Science, 2019
We frame the issue of pedestrian dynamics modeling in terms of path-integrals, a formalism origin... more We frame the issue of pedestrian dynamics modeling in terms of path-integrals, a formalism originally introduced in quantum mechanics to account for the behavior of quantum particles, later extended to quantum field theories and to statistical physics. Path-integration enables a trajectory-centric representation of the pedestrian motion, directly providing the probability of observing a given trajectory. This appears as the most natural language to describe the statistical properties of pedestrian dynamics in generic settings. In a given venue, individual trajectories can belong to many possible usage patterns and, within each of them, they can display wide variability. We provide first a primer on path-integration, and we introduce and discuss the path-integral functional probability measure for pedestrian dynamics in the diluted limit. As an illustrative example, we connect the path-integral description to a Langevin model that we developed previously for a particular crowd flow c...
The issue of human-induced load and related mechanical performance has become one of the leading ... more The issue of human-induced load and related mechanical performance has become one of the leading research topics in structural dynamics during the last decade. Although the concept of variability and uncertainty is well developed in structural dynamics disciplines such as wind, wave and earthquake engineering, most of the human-induced force models developed so far in structural engineering are deterministic, despite the intrinsic randomness of the crowd behaviour. The probabilistic models proposed in the last years have recognized two main sources of uncertainties, namely the structural system and the human-induced force. The pedestrian-related random variables usually considered in the cited force models are walking frequency, step length, free walking speed, single pedestrian force magnitude and body weight. According to the authors, another source of uncertainty should be considered, namely the one associated to the pedestrian traffic approaching and crossing the footbridge. Thi...
A Langevin model for low density pedestrian dynamics
Depth driven people counting using deep region proposal network
2017 IEEE International Conference on Information and Automation (ICIA), 2017
People counting is a crucial subject in video surveillance application. Factors such as severe oc... more People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.
arXiv: Physics and Society, 2020
Modeling the behavior of pedestrians walking in crowds is an outstanding fundamental challenge, d... more Modeling the behavior of pedestrians walking in crowds is an outstanding fundamental challenge, deeply connected with the physics of flowing active matter. The strong societal relevance of the topic, for its relations with individual safety and comfort, sparked vast modeling efforts from multiple scientific communities. Yet, likely because of the technical difficulties in acquiring experimental data, models quantitatively reproducing (statistical) features of pedestrian flows are scarce. This contribution has a twofold aim. First, we consider a pedestrian dynamics modeling approach previously proposed by some of the authors and based on Langevin equations. We review the approach and show that in the undisturbed and in the pair-wise avoidance regimes (i.e., in absence of interactions between pedestrians and in case of avoidance of a single individual walking in the opposite direction) the model is in quantitative agreement with real-life high-statistics measurements. Second, moving t...
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Aug 1, 2017
Overhead depth map measurements capture sufficient amount of information to enable human experts ... more Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image. Many of the assumptions underpinning the handcrafted solutions do not hold in these cases and the multitude of exceptions are hard to model precisely. Deep Learning (DL) algorithms, on the other hand, do not require hand crafted solutions and are the current state-of-the-art in object localization in images. However, they require exceeding amount of annotations to produce successful models. In the case of object localization these annotations are difficult and time consuming to produce. In this work we present an approach for developing pedestrian localization models using DL algorithms with efficient weak supervision from an expert. We circumvent the need for annotation of large corpus of data by annotating only small amount of patches and relying on synthetic data augmentation as a vehicle for injecting expert knowledge in the model training. This approach of weak supervision through expert selection of representative patches, suitable transformations and synthetic data augmentations enables us to successfully develop DL models for pedestrian localization efficiently.
Fire Safety Journal, 2019
This paper presents the findings of the workshop "New approaches to evacuation modelling", which ... more This paper presents the findings of the workshop "New approaches to evacuation modelling", which took place on the 11 th of June 2017 in Lund (Sweden) within the Symposium of the International Association for Fire Safety Science (IAFSS). The workshop gathered international experts in the field of fire evacuation modelling from 19 different countries and was designed to build a dialogue between the fire evacuation modelling world and experts in areas outside of fire safety engineering. The contribution to fire evacuation modelling of five topics within research disciplines outside fire safety engineering (FSE) have been discussed during the workshop, namely 1) Psychology/Human Factors, 2) Sociology, 3) Applied Mathematics, 4) Transportation, 5) Dynamic Simulation and Biomechanics. The benefits of exchanging information between these two groups are here highlighted in light of the topic areas discussed and the feedback received by the evacuation modelling community during the workshop. This included the feasibility of development/application of modelling methods based on fields other than FSE as well as a discussion on their implementation strengths and limitations. Each subject area is here briefly presented and its links to fire evacuation modelling are discussed. The feedback received during the workshop is discussed through a set of insights which might be useful for the future developments of evacuation models for fire safety engineering.
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Papers by Alessandro Corbetta