Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center

Xiang, Jun; Liu, Na

doi:10.1155/2022/1990951

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Ensuring the well-being of individuals within areas is a priority for authorities and organizations. Deep learning algorithms enable real-time crowd monitoring in scenarios by detecting safety hazards and anomalies, Xiang and Liu [24]. For example, using learning models to estimate crowd density can provide insights for managing people flow and preventing overcrowding at public events or transportation hubs.…”

Section: Safety Monitoringmentioning

confidence: 99%

A Review of Deep Learning Techniques for Crowd Management

Khan,

Nusratullah,

Korai

2023

ijmst

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Crowd Management is extremely important for maintaining safety and order in areas, such as events, transportation hubs, and urban centers. In the years deep learning methods have become tools for dealing with the complexities associated with crowd management. The adoption of this technology represents a change in how we analyze, predict, and respond to crowd dynamics. Deep learning algorithms can process amounts of data using CNNs. Learn intricate patterns enabling the creation of advanced models that can anticipate crowd behavior, identify unusual occurrences, and optimize crowd flow. By utilizing information from surveillance cameras, social media platforms, and other sources these models can provide real-time insights that empower authorities to make decisions and take measures to ensure public safety. To sum up, integrating deep learning techniques into crowd management offers a path toward improving situational awareness and effectively addressing the challenges presented by large gatherings of people. Further research and application of these technologies have the potential for enhancing crowd.

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Section: Safety Monitoringmentioning

confidence: 99%

A Review of Deep Learning Techniques for Crowd Management

Khan,

Nusratullah,

Korai

2023

ijmst

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“…Crowd counting techniques have wide range of applications ranges from pedestrian detection from UAV for crowd flow detection [11,12], passenger flow detection in exhibition center [13] and bus [14], surveillance System to detect suspicious activities, Security System, crowd analysis to avoid any disaster in public event and traffic management, military applications and health-care applications as in Fig. 2.…”

Section: Crowd Counting Applicationsmentioning

confidence: 99%

“…The research provided a new way to overcome the need of high computing resources based on three important components: feature fusion, Bayesian Loss and datasets utilizing bounding-box annotations to boost the efficiency of the crowd-counting assignment. The Complete Local Binary Pattern (CLBP) is used in this study [13] to derive the properties of crowd aggregation. On the basis of this, the deep learning model is built to identify crowd gathering.…”

Section: A Deep Neural Networkmentioning

confidence: 99%

“…4-6. [50] 194.1 297.8 Switch CNN [67] 318.1 439.2 LR-CNN [13] 325.6 369.4 CAT-CNN [40] 235.5 324.8 CCTrans [34] 245.0 343.6 JCTNet [54] 222.9 306.5 LibraNet [62] 181.2 262.2 MLPCNN [37] 238.63 317.28 HRANet [56] 160.9 235.8 MANet [59] 240.8 311.5 AUCNN [58] 231.8 312.4 AMSNet [42] 236.5 319.2 ASANet [51] 185.5 268.3 [65] 136.26 240.83 DEAL [50] 100.1 166.5 JCTNet [37] 90 161 TransCrowd [33] 97 168 CCTrans [34] 92 158 LibraNet [62] 88.1 143.7 MLPCNN [37] 103.61 168.9 HRANet [56] 84.6 146.2 EDENet [38] 86.6 158.5 JCCL+R [53] 100 169 AUCNN [58] 112.3 195.6 AMSNet [42] 86.5 167.2 WSITrans [55] 86.5 140. From Tables II-IV and its corresponding graph Figs.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

George,

2023

JAIT

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Crowd counting system has gained significant attention in recent years due to its relevance in various domains such as urban planning, public safety, resource allocation and decision-making in crowded environments. Due to differences in crowd densities, occlusions, size changes, and perspective distortions that are frequently seen in realworld scenarios, the system, nevertheless, falls short in terms of its purpose. To address this, it is necessary to create advanced neural network architectures, efficient methods for gathering and annotating data, reliable training procedures, and assessment criteria that accurately reflect the effectiveness of crowd counting systems. Therefore, the purpose of this study is to provide a comprehensive review of the state of the art in neural network-based crowd counting systems. The developments in neural network based crowd counting procedures, along with their features and limitations, most widely datasets and evaluation criteria are explored. The experimental findings of recent crowd counting systems are also examined. Hence, this work serves as an inspiration for additional research and development in this area, ultimately advancing crowd analysis and management systems.

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“…The authors in [21] developed a new crowd density estimation model by the use of DL models for passenger flow recognition model in exhibition centers. At the initial stage, the difference amplitude feature and gray feature of the central pixel are derived to create the CLBP feature for obtaining more crowd-group description information.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

et al. 2022

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Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

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Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center

Cited by 10 publications

References 27 publications

A Review of Deep Learning Techniques for Crowd Management

A Review of Deep Learning Techniques for Crowd Management

Neural Network-Based Crowd Counting Systems: State of the Art, Challenges, and Perspectives

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

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