Scalable Detection of Crowd Motion Patterns

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Abstract

Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing such crowds, one is often interested in the long-term macro-level motions of the crowd, as opposed to the micro-level individual movements at each moment in time. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within the crowd. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time using a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns from such scenarios which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.
Original languageEnglish
Pages (from-to)152-164
Number of pages13
JournalIEEE transactions on knowledge and data engineering
Volume32
Issue number1
Early online date1 Nov 2018
DOIs
Publication statusPublished - Jan 2020

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Pattern recognition
Macros
Pipelines
Motion
Costs
Experiments
Proximity Graphs
Moment
Spatial Relations
Scenarios
Spatial Pattern
Pattern Recognition
Person
Movement
Evaluation
Experiment

Keywords

  • Crowd Analytics
  • Pedestrian Dynamics
  • Spatio-Temporal Clustering
  • Trajectory Clustering
  • Proximity Graph

Cite this

@article{d7b8ca046d354860a8bca7d6a0bb5143,
title = "Scalable Detection of Crowd Motion Patterns",
abstract = "Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing such crowds, one is often interested in the long-term macro-level motions of the crowd, as opposed to the micro-level individual movements at each moment in time. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within the crowd. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time using a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns from such scenarios which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.",
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Scalable Detection of Crowd Motion Patterns. / Heldens, Stijn Jespah; Litvak, Nelli Vladimirovna; van Steen, Martinus Richardus.

In: IEEE transactions on knowledge and data engineering, Vol. 32, No. 1, 01.2020, p. 152-164.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Heldens, Stijn Jespah

AU - Litvak, Nelli Vladimirovna

AU - van Steen, Martinus Richardus

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AB - Studying the movements of crowds is important for understanding and predicting the behavior of large groups of people. When analyzing such crowds, one is often interested in the long-term macro-level motions of the crowd, as opposed to the micro-level individual movements at each moment in time. A high-level representation of these motions is thus desirable. In this work, we present a scalable method for detection of crowd motion patterns, i.e., spatial areas describing the dominant motions within the crowd. For measuring crowd movements, we propose a fast, scalable, and low-cost method based on proximity graphs. For analyzing crowd movements, we utilize a three-stage pipeline: (1) represents the behavior of each person at each moment in time using a low-dimensional data point, (2) cluster these data points based on spatial relations, and (3) concatenate these clusters based on temporal relations. Experiments on synthetic datasets reveals our method can handle various scenarios including curved lanes and diverging flows. Evaluation on real-world datasets shows our method is able to extract useful motion patterns from such scenarios which could not be properly detected by existing methods. Overall, we see our work as an initial step towards rich pattern recognition.

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