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A Real-Time Deep Network for Crowd Counting [article]

Xiaowen Shi, Xin Li, Caili Wu, Shuchen Kong, Jing Yang, Liang He
2020 arXiv   pre-print
In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters.  ...  Previous approaches for crowd counting have already achieved promising performance across various benchmarks.  ...  CONCLUSIONS In this paper, we present a compact CNN for crowd counting to deal with the lack of real-time performance of existing methods.  ... 
arXiv:2002.06515v1 fatcat:36bnz4weifhzjciicapgx5sl4y

Near Real-time Crowd Counting using Deep Learning Approach

Ujwala Bhangale, Suchitra Patil, Vaibhav Vishwanath, Parth Thakker, Amey Bansode, Devesh Navandhar
2020 Procedia Computer Science  
A deep convolution neural network (DCNN) based system can be used for near real-time crowd counting.  ...  In the current digital era, at many places crowd counting mechanisms still rely on old-fashioned methods such as maintaining registers, making use of people counters and sensors based counting at entrance  ...  In this paper, end-to-end solution is devised by providing a real time crowd counting mechanism for highly congested areas.  ... 
doi:10.1016/j.procs.2020.04.084 fatcat:ghg4jt72trc5rijsnc7bu3lrze

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

Akbar Khan, Jawad Ali Shah, Kushsairy Kadir, Waleed Albattah, Faizullah Khan
2020 Applied Sciences  
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  ...  Project title: Intelligent Real-Time Crowd Monitoring System Using Unmanned Aerial Vehicle (UAV) Video and Global Positioning Systems (GPS) Data.  ... 
doi:10.3390/app10144781 fatcat:i7x5c6sdo5cdrfwtrszsdvixsm

Crowd Density Estimation from Autonomous Drones Using Deep Learning: Challenges and Applications

A F M Saifuddin Saif, Faculty of Science, University of Helsinki, Finland, Zainal Rasyid Mahayuddin, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
2021 Journal of Engineering and Science Research  
Besides, comprehensive performance evaluation for existing methods using recent deep learning frameworks is illustrated for crowd counting purposes.  ...  which encourage researchers to move towards deep learning and computer vision techniques to minimize the need of human operator and thus develop intelligent crowd counting techniques.  ...  ACKNOWLEDGMENTS The authors would like to thank Universiti Kebangsaan Malaysia for providing financial support under the "Geran Universiti Penyelidikan" research grant, GUP-2020-064.  ... 
doi:10.26666/rmp.jesr.2021.6.1 fatcat:jugz7pefyrbaldgbr26aqt76om

Performance Comparison and Analysis for Large-scale Crowd Counting based on Convolutional Neural Networks

Reem Alotaibi, Bander Alzahrani, Rui Wang, Tarik Alafif, Ahmed Barnawi, Long Hu
2020 IEEE Access  
analysis and comparison with modern deep CNN networks, including those developed specifically for crowd counting using a massive crowd dataset, UCF-QNFR. • We carry out verification experiments based  ...  For network-based models, the smaller the MAE, testing time and model size, lead to a better model.  ... 
doi:10.1109/access.2020.3037395 fatcat:ql5qiuzomrcjfo47qkv3mvfdpi

Crowd Management and Monitoring using Deep Convolutinal Neural Network [chapter]

Pratiksha Singh, A. K. Daniel
2021 SCRS Conference Proceedings on Intelligent Systems  
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  ...  This paper proposed Deep Convolutional Neural Network, and Support Vector Machine. The data set are taken from Mall, Kumbh Mela, and UCFD.  ...  It has been used to count the ruse of real-time.  ... 
doi:10.52458/978-93-91842-08-6-15 fatcat:v4tgwe7egneqbniaxxsy6aeyfu

Deep Learning-Based Crowd Scene Analysis Survey

Sherif Elbishlawi, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, Mostafa M. Mohamed
2020 Journal of Imaging  
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.  ...  In [76] , the authors proposed a statistics-based approach for real-time detection of violent behaviors in a crowded scene.  ... 
doi:10.3390/jimaging6090095 pmid:34460752 fatcat:qomcb354xrh4xdg7f5ac4qvujq

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

G. Sreenu, M. A. Saleem Durai
2019 Journal of Big Data  
The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions.  ...  The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints.  ...  behavior of crowd in real time.  ... 
doi:10.1186/s40537-019-0212-5 fatcat:mh7d5d5c5zeczf5sdmgwz3claq

DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks [article]

Rui Wang, Min Chen, Nadra Guizani, Yong Li, Hamid Gharavi, Kai Hwang
2020 arXiv   pre-print
In addition, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example.  ...  This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks.  ...  The common method for crowd counting is a deep network that processes the image to a density map. The crowd counting will be then estimated by a summation over the predicted density map.  ... 
arXiv:2007.10878v1 fatcat:f2ybqkx2onahpiolobqx4o3hxe

A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis

Lina Li, Wei Wang
2020 Complexity  
In this paper, we analyze and calculate the crowd density in a tourist area utilizing video surveillance dynamic information analysis and divide the crowd counting and density estimation task into three  ...  network parameters using an intermediate supervision strategy.  ...  process the image signals containing crowd scenes in real time.  ... 
doi:10.1155/2020/6635446 fatcat:p5mljv2fvjhk5lbl6s5nb4nlhi

Neural Networks and Learning Systems for Human Machine Interfacing

Dr. Zhaojie Ju, Prof. Jinguo Liu, Prof. YongAn Huang, Prof. Naoyuki Kubota, Prof. John Q. Gan
2019 Neurocomputing  
Acknowledgments We would like to thank all reviewers for their timely and insightful comments for this special issue.  ...  We also would like to express our appreciation to the editor-in-chief and editorial office of the Neurocomputing (Elsevier) journal for their strong support.  ...  The paper titled "Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel" develops a crowd counting algorithm to provide the counting information for a bus dispatch system  ... 
doi:10.1016/j.neucom.2019.10.058 fatcat:yz4zj6t72rcezejfixznn2c3p4

Performance of video processing at the edge for crowd-monitoring applications

Camille Bailas, Mark Marsden, Dian Zhang, Noel E. O'Connor, Suzanne Little
2018 2018 IEEE 4th World Forum on Internet of Things (WF-IoT)  
We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise  ...  Using a cloud-centric approach where data is funnelled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues.  ...  This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number 12/RC/2289 and 16/SP/3804 (Insight Centre for Data Analytics).  ... 
doi:10.1109/wf-iot.2018.8355170 dblp:conf/wf-iot/BallasMZOL18 fatcat:yjq2k7dxivbmvip2g3e3h7i3m4

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Naveed Ilyas, Ahsan Shahzad, Kiseon Kim
2019 Sensors  
Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd  ...  Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques  ...  for real-time crowd counting.  ... 
doi:10.3390/s20010043 pmid:31861734 pmcid:PMC6983207 fatcat:gvso42grpjbw5ptdb23sdfeuwu

Open Challenges for Crowd Density Estimation

Shaya A Alshaya
2020 International Journal of Advanced Computer Science and Applications  
This issue presents a point of start to the field of the estimation of the crowd based on density or counts.  ...  In this paper, we try not only to present a deep review of the different approaches/techniques used in the previous works to estimate the size of the crowd but also to describe the different datasets used  ...  According to this deep study, the crowd size's estimation still requests enhancement in accuracy and real-time constraints. attempt to count persons by employing CNN.  ... 
doi:10.14569/ijacsa.2020.0110123 fatcat:5e7qgq4qsvhpxli2ukry455mh4

Crowd understanding and analysis

Qi Wang, Bo Liu, Jianzhe Lin
2021 IET Image Processing  
These social activities are often attended by a wide range of people, which puts forward high requirements for effective management and ensures the safety of the people involved in the activities.  ...  Currently, it has a wide range of applications in the fields of economy, public security, and so forth.  ...  real-time human detection in crowded scenes.  ... 
doi:10.1049/ipr2.12379 fatcat:shshhjjoxngotplvg7xzefpsne
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