Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Khan, Akbar Ali; Shah, Jawad Ali; Kadir, Kushsairy; Albattah, Waleed; Khan, Faizullah

doi:10.3390/app10144781

Cited by 34 publications

(23 citation statements)

References 88 publications

(107 reference statements)

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“…𝐴𝑐𝑡𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 = 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 + 𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑖𝑡𝑖𝑣𝑒 (7) where True Positive, False Positive and False Negative. Normally, box level Precision, Recall, and Fmeasure are utilized for crowd localization tasks.…”

Section: 𝐹 − 𝑀𝑒𝑎𝑠𝑢𝑟𝑒 =mentioning

confidence: 99%

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Crowd Management and Monitoring Using Deep Convolutinal Neural Network

Singh¹,

Daniel²

2021

SCRS Conference Proceedings on Intelligent Systems

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India is counted as one of the most populated countries in the world. A lot of crime is also increasing due to the increasing population, as criminal activities are more frequent in a crowded place. Being crowded is also facing a lot of diseases. Therefore, crowd management and monitoring are very important therefore viewed from the security, crowd management, and monitoring plays a very important role in identifying group/ individual’s behavior in a crowd using video and image sequence for counting the person and detection of such misbehavior elements, This paper proposed a model for crowd management and monitoring person counting as object detection techniques. This paper proposed Deep Convolutional Neural Network, and Support Vector Machine. The data set are taken from Mall, Kumbh Mela, and UCFD. The performance of the model using training and testing of data is improved.

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Section: 𝐹 − 𝑀𝑒𝑎𝑠𝑢𝑟𝑒 =mentioning

confidence: 99%

“…Several methods of technology are used to count congestion. There are mainly two types of crowd displays, structure crowds and unstructured crowds [7]. In many areas such as markets, towns, college campuses, hospitals, airports, stadiums, shopping malls and cultural and religious places [1], crowds are monitored by video cameras.…”

Section: Introductionmentioning

confidence: 99%

Crowd Management and Monitoring Using Deep Convolutinal Neural Network

Singh¹,

Daniel²

2021

SCRS Conference Proceedings on Intelligent Systems

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“…The most researches have verified the classification effectiveness of using DP types like CNN and recurrent neural network (RNN). CNN and RNN are applied for analyzing the crowd behavior and classifying digital images or videos [17][18][19][20][21][22]. The most important religious occasions for Muslims are Hajj and Umrah, which gathers high density crowds in the holy sites as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

Faragallah¹,

Alshamrani²,

El‐Hoseny³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the new cases rate like maintaining social distancing via crowd estimation. This manuscript presents an efficient detection model for the crowd counting and social distancing between visitors in the two holy mosques, Al Masjid Al Haram in Mecca and the Prophet's Mosque in Medina. Also, the manuscript develops a secure crowd monitoring structure based on the convolutional neural network (CNN) model using real datasets of images for the two holy mosques. The proposed framework is divided into two procedures, crowd counting and crowd recognition using datasets of different densities. To confirm the effectiveness of the proposed model, some metrics are employed for crowd analysis, which proves the monitoring efficiency of the proposed model with superior accuracy. Also, it is very adaptive to different crowd density levels and robust to scale changes in several places.

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“…In general, the group detection and density estimation have proven useful for corresponding steps of intelligent analytics and several applications. The review article [ 1 ] divides the counting of individuals into three categories; object-based counting, clustered counting, and regression-based counting.…”

Section: Introductionmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Khan

Albattah

Khan

et al. 2020

Sensors

Self Cite

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Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

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Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

Cited by 34 publications

References 88 publications

Crowd Management and Monitoring Using Deep Convolutinal Neural Network

Crowd Management and Monitoring Using Deep Convolutinal Neural Network

Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

Advances and Trends in Real Time Visual Crowd Analysis

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