Готові списки джерел за темами / Crowd Density Estimation

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Добірка наукової літератури з теми "Crowd Density Estimation"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Crowd Density Estimation"

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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Дисертації з теми "Crowd Density Estimation"

1

Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/369121.

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Анотація:
The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniqu
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2

Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3782/1/PhD_Thesis_Harikumar.pdf.

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Анотація:
The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniq
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3

Peduzzi, Alicia. "Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39456.

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Анотація:
The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ meas
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4

CHIU, HSIEN-CHUN, and 邱顯峻. "Crowd Density Estimation Based on Local Texture Features." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3kgm64.

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Анотація:
碩士<br>國立高雄大學<br>電機工程學系碩博士班<br>107<br>With the increase of population, there is a rising demand for smart visual surveillance. Therefore, crowd density estimation is an important research topic for image-based crowd monitoring. In this paper, we propose an improved robust center-symmetric local ternary pattern for crowd density estimation. We extract texture features from crowd images and then perform recognition by using a support vector machine. Experimental results on PETS 2009 dataset and Mall Dataset are given to illustrate the feasibility of the proposed approach f
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5

Lee, Chih-Yuan, and 李治原. "Abnormal Crowd Behavior Detection and Localization via Kernel Based Direct Density Ratio Estimation." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/95546677836042558374.

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Анотація:
碩士<br>國立臺灣科技大學<br>電子工程系<br>102<br>In this theme, we consider the analysis of abnormally behavior in surveillance system. To simplify the problem, we formalized it as an outlier detection problem. In our case, all behaviors in training data are normal. By creating a model by training data, we can define abnormalities whose probability is below a certain threshold under this model. Based on this, we use Kullback–Leibler importance estimation procedure (KLIEP) to compute the ratio of training data and testing data which we used as our inlier score. The KLIEP is a method to estimate the inlier sc
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6

Li, Guan-yao, and 李冠耀. "Estimating Crowd Flow and Crowd Density from Cellular Data for Mass Rapid Transit." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ef2s6p.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>105<br>Understanding the crowd flow and crowd density is crucial for smart city and urban planning. In this paper, we focus on the study of Mass rapid transit (MRT) that is playing an increasingly important role in many cities. The traditional way to estimate the crowd density and the crowd flow is by using smart card data. However, we can only know the number of passengers entering or exiting the station from smart card data. When and where the passengers change their MRT lines still remain unknown. Nowadays, each user has his/her own mobile phones and from the c
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Частини книг з теми "Crowd Density Estimation"

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Marana, A. N., M. A. Cavenaghi, R. S. Ulson, and F. L. Drumond. "Real-Time Crowd Density Estimation Using Images." In Advances in Visual Computing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11595755_43.

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2

Wang, Shunzhou, Huailin Zhao, Weiren Wang, Huijun Di, and Xueming Shu. "Improving Deep Crowd Density Estimation via Pre-classification of Density." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_27.

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Li, Meng, Tao Chen, Zhihua Li, and Hezi Liu. "An Efficient Crowd Density Estimation Algorithm Through Network Compression." In Springer Proceedings in Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55973-1_21.

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Nemade, Neeta A., and V. V. Gohokar. "Crowd Density as Dynamic Texture: Behavior Estimation and Classification." In Information and Communication Technology. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5508-9_9.

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5

Guo, Huimin, Fujin He, Xin Cheng, Xinghao Ding, and Yue Huang. "Pay Attention to Deep Feature Fusion in Crowd Density Estimation." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_39.

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6

Zhang, Guangdong, and Yan Piao. "Research and Realization of Crowd Density Estimation Based on Video." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70990-1_69.

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7

Krisp, Jukka M., Stefan Peters, and Florian Burkert. "Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation." In Lecture Notes in Geoinformation and Cartography. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32714-8_17.

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8

Nemade, Neeta Anil, and V. V. Gohokar. "An Efficient Approach to Feature Extraction for Crowd Density Estimation." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1513-8_36.

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Gupta, Ishakshi, and K. R. Seeja. "Crowd Density Estimation for Video Surveillance Using Deep Learning: A Review." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1329-5_23.

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10

Yin, Jia Hong, Sergio A. Velastin, and Anthony C. Davies. "Image processing techniques for crowd density estimation using a reference image." In Recent Developments in Computer Vision. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-60793-5_102.

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Тези доповідей конференцій з теми "Crowd Density Estimation"

1

Elharrouss, Omar, Hanadi Hassen Mohammed, Somaya Al-Maadeed, Khalid Abualsaud, Amr Mohamed, and Tamer Khattab. "Crowd density estimation with a block-based density map generation." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620151.

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2

Deokate, Sarika, Satpalsingh Rajput, Mahima Nair, Disha Parale, Asmita Mahamuni, and Priyanka Nalla. "Deep Learning-Based Crowd Surveillance and Density Estimation." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882436.

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Mallick, Sarbartha Sankar, Indrajit Das, Sreya Das, and Raja Karmakar. "An Intelligent Crowd Density and Motion Direction Estimation for Real-Time Crowd Dynamics." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723903.

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Roy, Amit, Nishant Jain, and Vikas Baghel. "Estimation of Abnormal Crowd Density Using Transfer Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725714.

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Ranasinghe, Yasiru, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara, and Vishal M. Patel. "CrowdDiff: Multi-Hypothesis Crowd Density Estimation Using Diffusion Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01217.

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Seo, Jinwook, Junyoung Choi, and Joohyun Lee. "Adaptive Transit Control with Crowd Density Estimation for UWB Contactless Gate." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615835.

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Tahira, Nusrat J., Patel D. Suresh, and Jang S. Park. "Deep Learning based Approach for Crowd Density Estimation and Flow Prediction." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773014.

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Shah, Malay, Sayal Goyal, and Sanket Salvi. "Crowd Density Estimation and Real-time Monitoring using Digital Twins in Transportation Hubs." In 2024 IEEE Pune Section International Conference (PuneCon). IEEE, 2024. https://doi.org/10.1109/punecon63413.2024.10895624.

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Gunduz, Ayse Elvan, Tugba Taskaya Temizel, and Alptekin Temizel. "Density estimation in crowd videos." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830356.

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Elharrouss, Omar, Somaya Al-Maadeed, and Khalid Abualsaud. "Crowd Density Estimation for Crowd Management at Event Entrance." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0241.

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Анотація:
Crowd management is an essential task to ensure the safety and smoothness of any events. Using the novel technologies including surveillance cameras, communication technics between security agents, the control of the crowd has become easier. However, the sue of these technics is still not perfectly effective. This paper presents an approach for managing the crowd at the entrance of event (festival, stadium...) using surveillance cameras. Using cameras and some panels in each entrance, the crowd density is extracted and illustrated in each panel. So, before reaching any gate, the people can see
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Звіти організацій з теми "Crowd Density Estimation"

1

Bell, Matthew A., and Marcel P. Huijser. Patterns of Domestic Animal-Vehicle Collisions on Tribal Lands in Montana, U.S. Western Transportation Institute, 2024. http://dx.doi.org/10.15788/1727735166.

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Анотація:
Animal-vehicle collisions (AVCs) are a significant concern for motorist safety and pose a risk to both wildlife and domestic animals. This report analyzes spatial patterns of wildlife-vehicle collisions (WVCs) and domestic animal-vehicle collisions (DAVCs) on Montana’s tribal lands to identify high-risk areas and inform mitigation strategies. Data from the Montana Department of Transportation (MDT) for large mammal carcasses (2008–2022) and reported crashes (2008–2020) were used to perform Kernel Density Estimation (KDE) and Getis-Ord Gi* (GOG) hotspot analyses for three tribal reservations wi
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