survey

Crowd Scene Understanding from Video: A Survey

Authors:

Jason M. Grant,

Patrick J. FlynnAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 2

Article No.: 19, Pages 1 - 23

https://doi.org/10.1145/3052930

Published: 27 March 2017 Publication History

Abstract

Crowd video analysis has applications in crowd management, public space design, and visual surveillance. Example tasks potentially aided by automated analysis include anomaly detection (such as a person walking against the grain of traffic or rapid assembly/dispersion of groups of people), population and density measurements, and interactions between groups of people. This survey explores crowd analysis as it relates to two primary research areas: crowd statistics and behavior understanding. First, we survey methods for counting individuals and approximating the density of the crowd. Second, we showcase research efforts on behavior understanding as related to crowds. These works focus on identifying groups, interactions within small groups, and abnormal activity detection such as riots and bottlenecks in large crowds. Works presented in this section also focus on tracking groups of individuals, either as a single entity or a subset of individuals within the frame of reference. Finally, a summary of datasets available for crowd activity video research is provided.

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Alhawsawi AKhan SUr Rehman F(2024)Crowd Counting in Diverse Environments Using a Deep Routing Mechanism Informed by Crowd Density LevelsInformation10.3390/info1505027515:5(275)Online publication date: 13-May-2024
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Index Terms

Crowd Scene Understanding from Video: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
        Scene anomaly detection
        Scene understanding

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 13, Issue 2

May 2017

226 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3058792

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 27 March 2017

Accepted: 01 January 2017

Revised: 01 January 2017

Received: 01 April 2016

Published in TOMM Volume 13, Issue 2

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https://doi.org/10.3390/info15050275
Al Mudawi NAnsar HAlazeb AAljuaid HAlQahtani YAlgarni AJalal ALiu H(2024)Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1D CNNFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2024.140180312Online publication date: 31-Jul-2024
https://doi.org/10.3389/fbioe.2024.1401803
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https://dl.acm.org/doi/10.1145/3663668
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