Open Collections

UBC Faculty Research and Publications

Deep Learning-Based Crowd Scene Analysis Survey Elbishlawi, Sherif; Abdelpakey, Mohamed H.; Eltantawy, Agwad; Shehata, Mohamed S.; Mohamed, Mostafa M.

Abstract

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

Item Metadata

Title
Deep Learning-Based Crowd Scene Analysis Survey
Creator
Elbishlawi, Sherif; Abdelpakey, Mohamed H.; Eltantawy, Agwad; Shehata, Mohamed S.; Mohamed, Mostafa M.
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2020-09-11
Description
Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
Subject
crowd scene; crowd counting; crowd action recognition; deep learning
Genre
Article
Type
Text
Language
eng
Date Available
2020-09-29
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0394547
URI
http://hdl.handle.net/2429/76204
Affiliation
Other UBC; Non UBC
Citation
Journal of Imaging 6 (9): 95 (2020)
Publisher DOI
10.3390/jimaging6090095
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

Item Media

Item Citations and Data

Rights

CC BY 4.0