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Статті в журналах з теми "Crowd Density Estimation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Mei, Ling, Mingyu Yu, Lvxiang Jia, and Mingyu Fu. "Crowd Density Estimation via Global Crowd Collectiveness Metric." Drones 8, no. 11 (2024): 616. http://dx.doi.org/10.3390/drones8110616.

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Анотація:

Drone-captured crowd videos have become increasingly prevalent in various applications in recent years, including crowd density estimation via measuring crowd collectiveness. Traditional methods often measure local differences in motion directions among individuals and scarcely handle the challenge brought by the changing illumination of scenarios. They are limited in their generalization. The crowd density estimation needs both macroscopic and microscopic descriptions of collective motion. In this study, we introduce a Global Measuring Crowd Collectiveness (GMCC) metric that incorporates intr

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2

Yi, Chuho, and Jungwon Cho. "Robust Estimation of Crowd Density Using Vision Transformers." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (2024): 1528–33. http://dx.doi.org/10.18517/ijaseit.14.5.11267.

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Estimating and predicting crowd density at large events or during disasters is of paramount importance for enhancing emergency management systems. This includes planning effective evacuation routes, optimizing rescue operations, and ensuring efficient deployment of emergency services. Traditionally, surveillance systems that rely on cameras have been employed to monitor crowd movements. However, accurately estimating crowd density using such systems presents several challenges. These challenges stem primarily from the interaction between large crowds and the limitations of two-dimensional came

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3

Meynberg, O., and G. Kuschk. "Airborne Crowd Density Estimation." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W3 (October 8, 2013): 49–54. http://dx.doi.org/10.5194/isprsannals-ii-3-w3-49-2013.

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4

Saharan, Ravi, Nishtha Kesswani, and Basant Aggarwal. "A method to estimate crowd size and crowd count." Journal of Information and Optimization Sciences 45, no. 4 (2024): 1105–15. http://dx.doi.org/10.47974/jios-1695.

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Анотація:

Crowd is known when people in large number gather together. When crowd gather for any reasons, it may cause any serious problem for administration. So authority must know about actual situation of crowd. In recent years need for crowd size estimation, has arisen to manage the crowd well. It can help in managing crowd before happening of huge event, traffic management, providing security to crowd and in many other areas where to manage crowd, some estimation on crowd size is needed. After reviewing existing applications, it can be concluded that there are many approaches for estimating density

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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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Анотація:

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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Анотація:

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

Quadri, Syeda Ruheena. "Automated Crowd Controlling System Using Image Processing and Video Processing Technique to Avoid Stamped." International Journal of Applied Evolutionary Computation 10, no. 3 (2019): 19–26. http://dx.doi.org/10.4018/ijaec.2019070103.

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Crowd control is needed to prevent the outbreak of disorder and prevent possible stampedes. An automated detection of people crowds from images has become a very important research field. Due to the importance of the topic, many researchers tried to solve this problem using CCTV street cameras. There are still significant problems in managing public pedestrian transport areas such as railway stations, stadiums, shopping malls, and religious gatherings. Using CCTV cameras, some image processing techniques are suitable for an automatic crowd monitoring system. The feasibility of such a system ha

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8

Xu, Xiaohang, Dongming Zhang, and Hong Zheng. "Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning." Journal of Electrical and Computer Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/2580860.

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Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it ad

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9

Sooksatra, Sorn, Toshiaki Kondo, Pished Bunnun, and Atsuo Yoshitaka. "Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection." Journal of Imaging 6, no. 5 (2020): 28. http://dx.doi.org/10.3390/jimaging6050028.

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Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a different object scale and crowd density. However, only high-level features are emphasized while ignoring low-level features. This paper proposes an estimation network by passing high-level features to shallow layers and emphasizing its low-level feature. Since an estimation network is a hierarchical network, a high-level feature is also emphasized by

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10

Yaoyao, Li Huailin Zhao Li Wang: SIT Shanghai. "An attention mechanism-based multi-scale network crowd density estimation algorithm." Journal of Information and Communication Engineering Volume 6, Issue 1 (2020): 359–46. https://doi.org/10.5281/zenodo.4261371.

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It is becoming more and more important to calculate the people number in terms of the requirement for the safety management, because that the crowd gathering scenes are common whether or not it is daily urban traffic or some special gatherings. Calculating the people number in high-density crowd is a very difficult challenge due to the diversity of ways people appear in crowded scenes. This paper proposes a multi-branch network which combines the dilated convolution and attention mechanism. By combining dilated convolution, the context information of different scales of the crowd image are ext

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11

Kok, Ven Jyn, and Chee Seng Chan. "Granular-based dense crowd density estimation." Multimedia Tools and Applications 77, no. 15 (2017): 20227–46. http://dx.doi.org/10.1007/s11042-017-5418-y.

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12

Xiang, Jun, and Na Liu. "Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center." Scientific Programming 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/1990951.

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Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blo

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13

Gouiaa, Rafik, Moulay A. Akhloufi, and Mozhdeh Shahbazi. "Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation." Big Data and Cognitive Computing 5, no. 4 (2021): 50. http://dx.doi.org/10.3390/bdcc5040050.

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Анотація:

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years,

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14

LIANG, GUOYUAN, KA KEUNG LEE, and YANGSHENG XU. "MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION." International Journal of Information Acquisition 04, no. 01 (2007): 1–14. http://dx.doi.org/10.1142/s0219878907001198.

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Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd den

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15

Zhang, Xingguo, Yinping Sun, Qize Li, Xiaodi Li, and Xinyu Shi. "Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS." ISPRS International Journal of Geo-Information 12, no. 2 (2023): 56. http://dx.doi.org/10.3390/ijgi12020056.

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Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd t

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16

Saif, A. F. M. Saifuddin, and Zainal Rasyid Mahayuddin. "Crowd Density Estimation from Autonomous Drones Using Deep Learning: Challenges and Applications." Journal of Engineering and Science Research 5, no. 6 (2021): 1–6. http://dx.doi.org/10.26666/rmp.jesr.2021.6.1.

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Crowd flow estimation from Drones or normally referred as Unmanned Aerial Vehicle (UAV ) for crowd management and monitoring is an essential research problem for adaptive monitoring and controlling dynamic crowd gatherings. Various challenges exist in this context, i.e. variation in density, scale, brightness, height from UAV platform, occlusion and inefficient pose estimation. Currently, gathering of crowd is mostly monitored by Close Circuit Television (CCTV) cameras where various problems exist, i.e. coverage in little area and constant involvement of human to monitor crowd which encourage

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17

Fitwi, Alem, Yu Chen, Han Sun, and Robert Harrod. "Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera." Computers 10, no. 11 (2021): 143. http://dx.doi.org/10.3390/computers10110143.

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Анотація:

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates

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18

Nan Dong, Fuqiang Liu, and Zhipeng Li. "Crowd Density Estimation Using Sparse Texture Features." Journal of Convergence Information Technology 5, no. 6 (2010): 125–37. http://dx.doi.org/10.4156/jcit.vol5.issue6.13.

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19

Yılmaz, Bedir, Siti Norul Huda Sheikh Abdullah, and Ven Jyn Kok. "Vanishing region loss for crowd density estimation." Pattern Recognition Letters 138 (October 2020): 336–45. http://dx.doi.org/10.1016/j.patrec.2020.08.001.

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20

Marana, A. N., S. A. Velastin, L. F. Costa, and R. A. Lotufo. "Automatic estimation of crowd density using texture." Safety Science 28, no. 3 (1998): 165–75. http://dx.doi.org/10.1016/s0925-7535(97)00081-7.

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21

Mliki, Hazar, Olfa Arous, and Mohamed Hammami. "Abnormal crowd density estimation in aerial images." Journal of Electronic Imaging 28, no. 01 (2019): 1. http://dx.doi.org/10.1117/1.jei.28.1.013047.

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22

Sharma, Vipul, Roohie Naaz Mir, and Chandrapal Singh. "Scale-aware CNN for crowd density estimation and crowd behavior analysis." Computers and Electrical Engineering 106 (March 2023): 108569. http://dx.doi.org/10.1016/j.compeleceng.2022.108569.

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23

Trung, Ha Duyen. "Estimation of Crowd Density Using Image Processing Techniques with Background Pixel Model and Visual Geometry Group." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 2 (2024): 142–54. https://doi.org/10.12928/biste.v6i2.10785.

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Crowd density estimation in complex backgrounds using a single image has garnered significant attention in automatic monitoring systems. In this paper, we propose a novel approach to enhance crowd estimation by leveraging the Bayesian Loss algorithm in conjunction with monitoring points and datasets such as UCF-QNRF, UCF_CC_50, and ShanghaiTech. The proposed method is evaluated using standard metrics including Mean Square Error (MSE) and Mean Absolute Error (MAE). Experimental results demonstrate that the proposed method achieves significantly improved accuracy compared to existing estimation

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24

Zhu, Liping, Hong Zhang, Sikandar Ali, Baoli Yang, and Chengyang Li. "Crowd counting via Multi-Scale Adversarial Convolutional Neural Networks." Journal of Intelligent Systems 30, no. 1 (2020): 180–91. http://dx.doi.org/10.1515/jisys-2019-0157.

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Abstract The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract t

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25

Zhang, Yani, Huailin Zhao, Zuodong Duan, Liangjun Huang, Jiahao Deng, and Qing Zhang. "Congested Crowd Counting via Adaptive Multi-Scale Context Learning." Sensors 21, no. 11 (2021): 3777. http://dx.doi.org/10.3390/s21113777.

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In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet

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26

Chauhan, Sarita. "Estimation of Crowd Density from UAV Images based on Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 242–48. http://dx.doi.org/10.22214/ijraset.2021.37324.

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Crowd monitoring is necessary to improve safety and controllable movements to minimize risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event etc. In this current digital age mostly crowd monitoring still relies on outdated methods such as keeping records, using people counters manually, and using sensors to count people at the entrance. These approaches are futile in situations where people's movements are completely unpredictable, highly variable, and complex. Crowd surveillance using unmanned aerial vehicles (UAVs), can help us solve these problems. The

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27

Yang, Yicheng, Jia Yu, Chenyu Wang, and Jiahong Wen. "Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data." Sustainability 14, no. 10 (2022): 6175. http://dx.doi.org/10.3390/su14106175.

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The urban open public spaces are the areas where people tend to gather together, which may lead to great crowd-gathering risk. This paper proposes a new method to assess the rank and spatial distribution of crowd-gathering risk in open public spaces in a large urban area. Firstly, a crowd density estimation method based on Tencent user density (TUD) data is built for different times in open public spaces. Then, a reasonable crowd density threshold is delimited to detect critical crowd situations in open public spaces and find out the key open public spaces that need to have intensive crowd-gat

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28

Ujlambkar, Dr Deepali. "Crowd Density Mapping and Anomaly Detection using YOLOv8 and DEEPSORT." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49725.

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Abstract: Effective crowd management in high-density public spaces remains a critical challenge, especially during large-scale events or emergencies. This study introduces a real-time system that integrates crowd density estimation and behavioral anomaly detection using deep learning and video surveillance. The proposed framework leverages YOLOv8 for high-precision person detection and DeepSORT for continuous multi-object tracking. Anomalies—such as panic movements, physical altercations, and prolonged immobility—are identified using motion trajectory analysis and rule-based behavioral classif

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29

Luo, Hongling, Jun Sang, Weiqun Wu, et al. "A High-Density Crowd Counting Method Based on Convolutional Feature Fusion." Applied Sciences 8, no. 12 (2018): 2367. http://dx.doi.org/10.3390/app8122367.

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In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environment, while there exists technology drawbacks and a lack of mature systems. Aiming to solve the crowd counting problem with high-density under complex environments, a feature fusion-based deep convolutional neural network method FF-CNN (Feature Fusion of Convolutional Neural Network) was proposed in this paper. The proposed FF-CNN mapped the crowd

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30

Ali, Salem Ali Bin Sama, and Salem Ali Bin Sama Hussein. "A HYBRID INTELLIGENT MODEL FOR CROWD DENSITY ESTIMATION." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 11 (2017): 127–39. https://doi.org/10.5281/zenodo.1067580.

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This work is aiming at the development of a hybrid intelligent model to tackle the problem of crowd density estimation. The presented hybrid model comprises Extreme Learning Machine (ELM) for pattern recognition, Differential Evolution (DE) for model construction, as well as texture feature extraction techniques for input pattern encoding. In this work, DE is adopted to design an efficient recognition model by performing training instances selection as well as ELM topology selection.  To assess the performances of the proposed model, a three popular crowd density benchmark dataset are use

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31

Huang, Liangjun, Shihui Shen, Luning Zhu, Qingxuan Shi, and Jianwei Zhang. "Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting." Sensors 22, no. 9 (2022): 3233. http://dx.doi.org/10.3390/s22093233.

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In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We

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32

J. Evangelin Deva Sheela. "Drone Based Crowd Density Estimation and Localization Using Temporal and Location Sensitive Fused Attention Model on Pyramid Features." Journal of Information Systems Engineering and Management 10, no. 38s (2025): 805–29. https://doi.org/10.52783/jisem.v10i38s.6969.

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Анотація:

Crowd monitoring is essential for security and effective management in public space, and drone imagery offers a powerful tool for this purpose. Though traditional methods often fall short in accuracy and efficiency techniques like manual counting, detection based approaches struggle with challenges like occlusion, low resolution, and high crowd density, leading to unreliable estimates. To address data privacy concerns related to capturing images of individuals without consent, regulatory barriers that restrict flight zones and operational guidelines, and technical limitations such as limited b

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33

Li, Yue, Tao Zou, and Peng Chen. "Estimation of Crowd Density Based on Adaptive LBP." Advanced Materials Research 998-999 (July 2014): 864–68. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.864.

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Анотація:

In recent years, mass incidents occurred frequently. In order to identify and warn the incidents proactively and timely. To this problem, we propose an algorithm based on adaptive LBP to estimate the crowd density. Firstly, use three-dimensional Hessian matrix to detect characteristic point. Secondly, use improved adaptive LBP to extract the dynamic texture and analyze it, then get the local feature. Thirdly, learn for global characteristic vectors, and then estimate the density level with support vector machine (SVM). Through simulation comparison, the density estimation method is more accura

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34

Xia, Yinfeng, Yuqiang He, Sifan Peng, Xiaoliang Hao, Qianqian Yang, and Baoqun Yin. "EDENet: Elaborate density estimation network for crowd counting." Neurocomputing 459 (October 2021): 108–21. http://dx.doi.org/10.1016/j.neucom.2021.06.086.

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35

Feng, Fujian, Shuang Liu, Yongzheng Pan, Xin He, Jiayin Wei, and Lin Wang. "Crowd density estimation method based on floor area." Journal of Physics: Conference Series 1651 (November 2020): 012060. http://dx.doi.org/10.1088/1742-6596/1651/1/012060.

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36

Rao, Aravinda S., Jayavardhana Gubbi, Slaven Marusic, and Marimuthu Palaniswami. "Estimation of crowd density by clustering motion cues." Visual Computer 31, no. 11 (2014): 1533–52. http://dx.doi.org/10.1007/s00371-014-1032-4.

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37

Zhou, Bingyin, Fan Zhang, and Lizhong Peng. "Higher-order SVD analysis for crowd density estimation." Computer Vision and Image Understanding 116, no. 9 (2012): 1014–21. http://dx.doi.org/10.1016/j.cviu.2012.05.005.

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38

Fu, Min, Pei Xu, Xudong Li, Qihe Liu, Mao Ye, and Ce Zhu. "Fast crowd density estimation with convolutional neural networks." Engineering Applications of Artificial Intelligence 43 (August 2015): 81–88. http://dx.doi.org/10.1016/j.engappai.2015.04.006.

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39

Razavi, Mahnaz, Hadi Sadoghi Yazdi, and Amir Hossein Taherinia. "Crowd analysis using Bayesian Risk Kernel Density Estimation." Engineering Applications of Artificial Intelligence 82 (June 2019): 282–93. http://dx.doi.org/10.1016/j.engappai.2019.04.011.

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40

Negied, Nermin Kamal Abdel-Wahab, Elsayed B. Hemayed, and Magda Fayek. "HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (2016): 1655025. http://dx.doi.org/10.1142/s0218001416550259.

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This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio

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41

Yang, Kai-Wei, Yen-Yun Huang, Jen-Wei Huang, et al. "Improving Crowd Density Estimation by Fusing Aerial Images and Radio Signals." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (2022): 1–23. http://dx.doi.org/10.1145/3492346.

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A recent line of research focuses on crowd density estimation from RGB images for a variety of applications, for example, surveillance and traffic flow control. The performance drops dramatically for low-quality images, such as occlusion, or poor light conditions. However, people are equipped with various wireless devices, allowing the received signals to be easily collected at the base station. As such, another line of research utilizes received signals for crowd counting. Nevertheless, received signals offer only information regarding the number of people, while an accurate density map canno

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42

Liu, Zelong, Xin Zhou, Tao Zhou, and Yuanyuan Chen. "Foreground Segmentation-Based Density Grading Networks for Crowd Counting." Sensors 23, no. 19 (2023): 8177. http://dx.doi.org/10.3390/s23198177.

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Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safety and urban planning. Among the various object counting tasks, crowd counting is particularly notable for its critical role in social security and urban planning. However, intricate backgrounds in images often lead to misidentifications, wherein the complex background is mistaken as the foreground, thereby inflating forecasting errors. Additionally

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43

Hou, Xiaoyu, Jihui Xu, Jinming Wu, and Huaiyu Xu. "Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning." Applied Sciences 11, no. 24 (2021): 12037. http://dx.doi.org/10.3390/app112412037.

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Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data

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44

Negied, Nermin K., Ayman El-Sayed, and Asmaa S. Hassaan. "Automated Decision Technique for the Crowd Estimation Method Using Thermal Videos." Applied Computational Intelligence and Soft Computing 2022 (December 1, 2022): 1–16. http://dx.doi.org/10.1155/2022/7782879.

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Counting and detecting the pedestrians is an important and critical aspect for several applications such as estimation of crowd density, organization of events, individual’s flow control, and surveillance systems to prevent the difficulties and overcrowding in a huge gathering of pedestrians such as the Hajj occasion, which is the annual event for Muslims with the growing number of pilgrims every year. This paper is based on applying some enhancements to two different techniques for automatically estimating the crowd density. These two approaches are based on individual motion and the body’s t

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45

Wei, Xinlei, Junping Du, Meiyu Liang, and Zhe Xue. "Crowd Density Field Estimation Based on Crowd Dynamics Theory and Social Force Model." Chinese Journal of Electronics 28, no. 3 (2019): 521–28. http://dx.doi.org/10.1049/cje.2019.03.021.

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Li, Bo, Hongbo Huang, Ang Zhang, Peiwen Liu, and Cheng Liu. "Approaches on crowd counting and density estimation: a review." Pattern Analysis and Applications 24, no. 3 (2021): 853–74. http://dx.doi.org/10.1007/s10044-021-00959-z.

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47

Liu, Yan-Bo, Rui-Sheng Jia, Jin-Tao Yu, Ruo-Nan Yin, and Hong-Mei Sun. "Crowd density estimation via a multichannel dense grouping network." Neurocomputing 449 (August 2021): 61–70. http://dx.doi.org/10.1016/j.neucom.2021.03.078.

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48

杨, 清永. "A Crowd Density Estimation Based on Improved Kalman Filtering." Computer Science and Application 12, no. 04 (2022): 981–87. http://dx.doi.org/10.12677/csa.2022.124101.

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49

Yao, Hai-Yan, Wang-Gen Wan, and Xiang Li. "Mask Guided GAN for Density Estimation and Crowd Counting." IEEE Access 8 (2020): 31432–43. http://dx.doi.org/10.1109/access.2020.2973333.

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Pan, Shao-Yun, Jie Guo, and Zheng Huang. "An Improved Convolutional Neural Network on Crowd Density Estimation." ITM Web of Conferences 7 (2016): 05009. http://dx.doi.org/10.1051/itmconf/20160705009.

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