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Title: Crowd monitoring and localization using deep convolutional neural network: a review
Authors: Khan, Akbar
Shah, Jawad Ali
Kadir, Kushsairy
Albattah, Waleed
Khan, Faizullah
Keywords: Crowd behavior
Crowd counting
Crowd density estimation
Crowd monitoring
Deep convolutional neural networks (DCNN)
Issue Date: Jul-2020
Publisher: MDPI AG
Citation: Akbar Khan, Jawad Ali Shah, Kushsairy Kadir, Waleed Albattah, Faizullah Khan, Crowd monitoring and localization using deep convolutional neural network: A review, Volume 10, Issue 14, July 2020, doi: 10.3390/app10144781
Abstract: Crowd management and monitoring is crucial for maintaining public safety and is an important research topic. Developing a robust crowd monitoring system (CMS) is a challenging task as it involves addressing many key issues such as density variation, irregular distribution of objects, occlusions, pose estimation, etc. Crowd gathering at various places like hospitals, parks, stadiums, airports, cultural and religious points are usually monitored by Close Circuit Television (CCTV) cameras. The drawbacks of CCTV cameras are: limited area coverage, installation problems, movability, high power consumption and constant monitoring by the operators. Therefore, many researchers have turned towards computer vision and machine learning that have overcome these issues by minimizing the need of human involvement. This review is aimed to categorize, analyze as well as provide the latest development and performance evolution in crowd monitoring using different machine learning techniques and methods that are published in journals and conferences over the past five years. © 2020 by the authors.used. In general, specific path loss condition can affect the MANET routing protocol performance. This study provides a comparative result using NS3 of two extreme routing protocol approach, i.e. pro-active and reactive which can be used as a reference point for further development of routing protocol in various ad hoc network applications and scenarios.
Description: The article is indexed by Scopus
ISSN: 20763417
Appears in Collections:Journal Articles

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