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Journal articles

Academic literature on the topic 'Crowd dataset'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Crowd dataset"

1

Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, et al. "Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage." Sensors 22, no. 14 (2022): 5102. http://dx.doi.org/10.3390/s22145102.

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Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating add

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2

Bhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, et al. "A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network." PeerJ Computer Science 8 (March 25, 2022): e895. http://dx.doi.org/10.7717/peerj-cs.895.

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This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully c

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3

Alafif, Tarik, Anas Hadi, Manal Allahyani, et al. "Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds." Electronics 12, no. 5 (2023): 1165. http://dx.doi.org/10.3390/electronics12051165.

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Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors

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4

Ren, Guoyin, Xiaoqi Lu, and Yuhao Li. "Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis." Journal of Sensors 2022 (August 12, 2022): 1–19. http://dx.doi.org/10.1155/2022/7247757.

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Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET

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5

BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, et al. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (January 14, 2022): 1190. http://dx.doi.org/10.12688/f1000research.73156.2.

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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Network

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6

BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, et al. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (November 24, 2021): 1190. http://dx.doi.org/10.12688/f1000research.73156.1.

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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Net

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7

Wu, Junfeng, Zhiyang Li, Wenyu Qu, and Yizhi Zhou. "One Shot Crowd Counting with Deep Scale Adaptive Neural Network." Electronics 8, no. 6 (2019): 701. http://dx.doi.org/10.3390/electronics8060701.

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This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd

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8

Kaya, Abdil, Stijn Denis, Ben Bellekens, Maarten Weyn, and Rafael Berkvens. "Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation." Data 5, no. 2 (2020): 52. http://dx.doi.org/10.3390/data5020052.

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Organisers of events attracting many people have the important task to ensure the safety of the crowd on their venue premises. Measuring the size of the crowd is a critical first step, but often challenging because of occlusions, noise and the dynamics of the crowd. We have been working on a passive Radio Frequency (RF) sensing technique for crowd size estimation, and we now present three datasets of measurements collected at the Tomorrowland music festival in environments containing thousands of people. All datasets have reference data, either based on payment transactions or an access contro

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9

Shao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang, and Huayi Zhan. "A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV." Sensors 20, no. 19 (2020): 5550. http://dx.doi.org/10.3390/s20195550.

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Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent

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10

Zhang, Cong, Kai Kang, Hongsheng Li, Xiaogang Wang, Rong Xie, and Xiaokang Yang. "Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset." IEEE Transactions on Multimedia 18, no. 6 (2016): 1048–61. http://dx.doi.org/10.1109/tmm.2016.2542585.

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