Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

Kroneman, Werner; Corbetta, Alessandro; Toschi, Federico

doi:10.17815/cd.2020.30

Cited by 13 publications

(15 citation statements)

References 12 publications

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“…Large-volumes of experimental data, in the order of hundred of thousands real-life trajectories, are indeed essential in order to analyze quantitatively and systematically the physics of pedestrian motion, disentangling the high variations in individual behaviors from average patterns, and characterizing typical fluctuations and universal features [19,20]. This relative delay in performing high-statistics based analyses of pedestrian motion (especially in comparison with other "active matter" physical systems [21]), is most likely due to the complex technical challenge of achieving accurate, privacy-preserving, individual tracking in real-life conditions (see, e.g., [20,22,23], or [24] for approaches targeting even higher resolution). Market solutions, as the one considered in this paper, are also becoming accessible, offering various trade-offs between accuracy and costs (see, e.g., [25]).…”

Section: Related Work: (Social) Distance In Pedestrian Dynamicsmentioning

confidence: 99%

Monitoring physical distancing for crowd management: Real-time trajectory and group analysis

et al. 2020

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Physical distancing, as a measure to contain the spreading of Covid-19, is defining a “new normal”. Unless belonging to a family, pedestrians in shared spaces are asked to observe a minimal (country-dependent) pairwise distance. Coherently, managers of public spaces may be tasked with the enforcement or monitoring of this constraint. As privacy-respectful real-time tracking of pedestrian dynamics in public spaces is a growing reality, it is natural to leverage on these tools to analyze the adherence to physical distancing and compare the effectiveness of crowd management measurements. Typical questions are: “in which conditions non-family members infringed social distancing?”, “Are there repeated offenders?”, and “How are new crowd management measures performing?”. Notably, dealing with large crowds, e.g. in train stations, gets rapidly computationally challenging. In this work we have a two-fold aim: first, we propose an efficient and scalable analysis framework to process, offline or in real-time, pedestrian tracking data via a sparse graph. The framework tackles efficiently all the questions mentioned above, representing pedestrian-pedestrian interactions via vector-weighted graph connections. On this basis, we can disentangle distance offenders and family members in a privacy-compliant way. Second, we present a thorough analysis of mutual distances and exposure-times in a Dutch train platform, comparing pre-Covid and current data via physics observables as Radial Distribution Functions. The versatility and simplicity of this approach, developed to analyze crowd management measures in public transport facilities, enable to tackle issues beyond physical distancing, for instance the privacy-respectful detection of groups and the analysis of their motion patterns.

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Section: Related Work: (Social) Distance In Pedestrian Dynamicsmentioning

confidence: 99%

Monitoring physical distancing for crowd management: Real-time trajectory and group analysis

et al. 2020

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“…We acquired individual pedestrian trajectories at 30 Hz time resolution by means of overhead depth images and the HA-HOG localization method [21]. We collected raw depth images of a walkable area of about 30 m 2 via 8 Orbbec Persee sensors attached underneath a pedestrian overpass, and arranged in a 4x2 grid (see dashed gray line in Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

Modeling Routing Choices in Unidirectional Pedestrian Flows

2022

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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.

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“…In the localization stage, each frame is processed independently to single out each pedestrian and estimate their position. To this purpose image processing and/or machine learning models are used [13,16,17,26]. Lagrangian time-tracking assigns an id to each detection on the basis of continuity arguments.…”

Section: Essentials Of 3d Optic-based Pedestrian Trackingmentioning

confidence: 99%

“…Localization occurs via depth clustering (as in [12]), and time-tracking uses the Trackpy Python library [28]. The same approach has been successfully used, e.g., in stations, streets, and museums [9,12,13,26]. The specific setup considered consists of a grid of 3 × 4 Microsoft Kinect TM [25] depth sensors.…”

Section: Tracking Technologies and Experimental Setupsmentioning

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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Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

Cited by 13 publications

References 12 publications

Monitoring physical distancing for crowd management: Real-time trajectory and group analysis

Monitoring physical distancing for crowd management: Real-time trajectory and group analysis

Modeling Routing Choices in Unidirectional Pedestrian Flows

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

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