Relevant bibliographies by topics / Analysis crowd

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Analysis crowd'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Analysis crowd"

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Husman, Muhammad Afif, Waleed Albattah, Zulkifli Zainal Abidin, et al. "Unmanned Aerial Vehicles for Crowd Monitoring and Analysis." Electronics 10, no. 23 (2021): 2974. http://dx.doi.org/10.3390/electronics10232974.

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Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for
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Elbishlawi, Sherif, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, and Mostafa M. Mohamed. "Deep Learning-Based Crowd Scene Analysis Survey." Journal of Imaging 6, no. 9 (2020): 95. http://dx.doi.org/10.3390/jimaging6090095.

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Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. 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. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene data
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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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R, Shaamili. "A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd." Computational Intelligence and Machine Learning 3, no. 2 (2022): 9–14. http://dx.doi.org/10.36647/ciml/03.02.a002.

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In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considerin
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Yugendar, Poojari, and K. V. R. Ravishankar. "Crowd Behavioural Analysis at a Mass Gathering Event." Journal of KONBiN 46, no. 1 (2018): 5–20. http://dx.doi.org/10.2478/jok-2018-0020.

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Abstract Religious occasions, gathering at fairs and terminals, are the events of crowd gatherings. Such gatherings act as severe threats for crowds because of high density in less space, which ends up in adverse outcomes resulting in crowd stampedes. The movement of an individual person in a crowd is influenced by the physical factors. In the present study, characteristics like age, gender, group size, child holding, child carrying, people with luggage and without luggage are considered for crowd behaviour analysis. The average speed of the crowd movement was observed as 0.86 m/s. The statist
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Andriyanto, Sidhiq, M. Suyanto, and Sukoco Sukoco. "Implementasi Metode Reynolds menggunakan Simulasi Kerumunan Bebek." INTENSIF 1, no. 2 (2017): 75. http://dx.doi.org/10.29407/intensif.v1i2.788.

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 "Simulation of Duck Crows Using Reynolds Method" is a study with the aim to find out the behavior of duck breeding crowd. The next goal is to make a crowd simulation using the Reynolds method. Limitations of this research variable is the object of research on adult duck Turi, the method used is Reynolds method. The simulations are made using Unity3D software in the form of 3D and the animation provided is just a running gesture. The method of analysis of this research is using research and development method. The result of the research is the data of duck walking in the crowd to be appl
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JOHANSSON, ANDERS, DIRK HELBING, HABIB Z. AL-ABIDEEN, and SALIM AL-BOSTA. "FROM CROWD DYNAMICS TO CROWD SAFETY: A VIDEO-BASED ANALYSIS." Advances in Complex Systems 11, no. 04 (2008): 497–527. http://dx.doi.org/10.1142/s0219525908001854.

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The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett.97 (2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E75 (2007) 046109]. Although the behavior of pedestrian crowds under extreme condit
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Aiello, Lucia. "Digital Skill Evolution in an Industrial Relationship." International Journal of R&D Innovation Strategy 1, no. 1 (2019): 1–15. http://dx.doi.org/10.4018/ijrdis.2019010101.

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Crowdsourcing is a powerful mechanism for doing online work and allows for collaboration. By now, in the media and in business on the Internet, crowdsourcing is recognized as an innovative form of value creation that needs taken seriously. This article provides a framework to propose the relation between crowds and tutorship; it considers the tutor of a crowd as a strategic professional figure in an online community. This is done by the consideration of the different roles, activities and tasks of a tutor through the field analysis of a platform of one company that uses crowdsourcing. This tut
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Kim, Eui Myoung, and Seung Chan Hong. "Crowd Density Analysis Using Crowd Simulation." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 43, no. 3 (2025): 297–304. https://doi.org/10.7848/ksgpc.2025.43.3.297.

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Denis, Stijn, Ben Bellekens, Abdil Kaya, Rafael Berkvens, and Maarten Weyn. "Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks." Sensors 20, no. 9 (2020): 2624. http://dx.doi.org/10.3390/s20092624.

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The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation er
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Dissertations / Theses on the topic "Analysis crowd"

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Mehran, Ramin. "Analysis of behaviors in crowd videos." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4801.

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In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity re
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Jan, Yasir. "Novel architectures for spectator crowd image analysis." Thesis, Jan, Yasir (2020) Novel architectures for spectator crowd image analysis. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/59147/.

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Crowd image analysis can involve various tasks, such as head detection, head pose estimation, and body detection. These tasks face a range of issues including low resolution, varying crowd density, overlapping bodies, and image corruptions. Additionally, the techniques performing these tasks have computational overhead as well. Existing head detection techniques perform poorly for very low-resolution images. They also cannot perform head detection and head pose estimation simultaneously for multiple heads. To address these issues, we propose a novel neural network architecture (WNet), which
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Holmer, Torsten, and Jörg Rainer Noennig. "Listening to the Crowd." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234390.

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In der Stadtplanung hat sich in der jüngeren Vergangenheit Partizipation als neues Paradigma durchgesetzt. Projektentwickler, Planer und Politiker haben erkannt, dass Bauprojekte mit großem Einfluss auf Stadtgesellschaft und Stadtentwicklung nicht mehr ohne umfassende Bürgerbeteiligung durchgeführt werden können. Vorfälle wie die Unruhen um das Bahnhofsprojekt Stuttgart 21 haben gezeigt, dass die regulären Verfahren der Bauleitplanung mit ihren Instrumenten der formalen Bürgerbeteiligung (Anzeige und Auslage von Planungsunterlagen) nicht ausreichen, um einen gesellschaftlichen Konsens zu kontr
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Holmer, Torsten, and Jörg Rainer Noennig. "Listening to the Crowd." TUDpress, 2017. https://tud.qucosa.de/id/qucosa%3A30888.

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In der Stadtplanung hat sich in der jüngeren Vergangenheit Partizipation als neues Paradigma durchgesetzt. Projektentwickler, Planer und Politiker haben erkannt, dass Bauprojekte mit großem Einfluss auf Stadtgesellschaft und Stadtentwicklung nicht mehr ohne umfassende Bürgerbeteiligung durchgeführt werden können. Vorfälle wie die Unruhen um das Bahnhofsprojekt Stuttgart 21 haben gezeigt, dass die regulären Verfahren der Bauleitplanung mit ihren Instrumenten der formalen Bürgerbeteiligung (Anzeige und Auslage von Planungsunterlagen) nicht ausreichen, um einen gesellschaftlichen Konsens zu kontr
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KHAN, SULTAN DAUD. "Automatic Detection and Computer Vision Analysis of Flow Dynamics and Social Groups in Pedestrian Crowds." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/102644.

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Computer vision played a vital role in the field of video surveillance. However, recent developed computer vision algorithms rarely solve the problems related to real time crowd management. The phenomena of crowd like sports, festivals, concerts, political gatherings etc, are mostly observed in urban areas, which attracts hundreds of thousands people. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. The main theme
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Guler, Puren. "Automated Crowd Behavior Analysis For Video Surveillance Applications." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614659/index.pdf.

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Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups<br>people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behaviors of individuals in a crowd (object based) and using this knowledge to make deductio
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CORBETTA, ALESSANDRO. "Multiscale Crowd Dynamics: Physical Analysis, Modeling and Applications." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2659720.

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In this thesis we investigate the dynamics of pedestrian crowds in a fundamental and applied perspective. Envisioning a quantitative understanding we employ ad hoc large-scale experimental measurements as well as analytic and numerical models. Moreover, we analyze current regulations in matter of pedestrians structural actions (structural loads), in view of the need of guaranteeing pedestrian safety in serviceable built environments. This work comes in three complementary parts, in which we adopt distinct perspectives and conceptually different tools, respectively from statistical phy
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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/369001.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1406/1/PhD_Thesis_Habib.pdf.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Bisagno, Niccol&#242. "On simulating and predicting pedestrian trajectories in a crowd." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/256722.

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Crowds of people are gathering at multiple venues, such as concerts, political rallies, as well as in commercial malls, or just simply walking on the streets. More and more people are flocking to live in urban areas, thus generating a lot of scenarios of crowds. As a consequence, there is an increasing demand for automatic tools that can analyze and predict the behavior of crowds to ensure safety. Crowd motion analysis is a key feature in surveillance and monitoring applications, providing useful hints about potential threats to safety and security in urban and public spaces. It is well known
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Books on the topic "Analysis crowd"

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author, Dellinger Elisabeth, ed. Beat the crowd: How you can out-invest the herd by thinking differently. Wiley, 2015.

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Plummer, Tony. The psychology of technical analysis: Profiting from crowd behavior and the dynamics of price. Probus Pub. Co., 1993.

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Ali, Saad, Ko Nishino, Dinesh Manocha, and Mubarak Shah, eds. Modeling, Simulation and Visual Analysis of Crowds. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8483-7.

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N, Pathak P., and Bhabha Atomic Research Centre, eds. Extraction chromatographic studies on a strontium selective crown ether. Bhabha Atomic Research Centre, 1998.

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Associates, Golder, and Canada Centre for Mineral and Energy Technology., eds. Crown pillar stability back-analysis: Report to CANMET. Golder Associates Ltd., 1990.

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Sunkin, Maurice. The nature of the crown: A legal and political analysis. Oxford University Press, 1999.

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Tonasket Ranger District (Wash.), Washington (State). Dept. of Ecology., and TerraMatrix Inc, eds. Crown Jewel Mine, draft environmental impact statement. TerraMatrix, 1995.

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Ontario. Ministry of Natural Resources. An environmental assessment of timber management on crown lands in the Megisan Lake area. The Committee, 1996.

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Cerrah, Ibrahim. Crowds and public order policing: An analysis of crowds and interpretations of their behaviour based on observational studies in Turkey, England, and Wales. Ashgate/Dartmouth, 1998.

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Treacy, Eoin. Crowd Money: A Practical Guide to Macro Behavioural Technical Analysis. Harriman House Publishing, 2013.

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Book chapters on the topic "Analysis crowd"

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Still, G. Keith. "RAMP analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-5.

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Still, G. Keith. "Crowd risk analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-9.

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Still, G. Keith. "Event egress analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-10.

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Still, G. Keith. "Strategic and tactical analysis." In Applied Crowd Science. CRC Press, 2021. http://dx.doi.org/10.1201/9781351053068-11.

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Amsterdamer, Yael, and Tova Milo. "Crowd Mining and Analysis." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80657-2.

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Amsterdamer, Yael, and Tova Milo. "Crowd Mining and Analysis." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80657.

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Ciyamala Kushbu, S., T. M. Inbamalar, Chettiyar Vani Vivekanand, B. Sarala, M. Amala Justus Selvam, and Krishna Kant Gupta. "Crowd analysis using deepSORT." In Data Science & Exploration in Artificial Intelligence. CRC Press, 2025. https://doi.org/10.1201/9781003589273-69.

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Ryan, David, Simon Denman, Sridha Sridharan, and Clinton Fookes. "Scene Invariant Crowd Counting and Crowd Occupancy Analysis." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28598-1_6.

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Feliciani, Claudio, Kenichiro Shimura, and Katsuhiro Nishinari. "Analysis of Past Crowd Accidents." In Introduction to Crowd Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90012-0_3.

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Wąs, Jarosław, and Krzysztof Kułakowski. "Social Groups in Crowd." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_255-1.

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Conference papers on the topic "Analysis crowd"

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Anand, Avinash, Sarthak Jain, Shashank Sharma, et al. "Pulse of the Crowd: Quantifying Crowd Energy through Audio and Video Analysis." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00056.

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Mehra, Gunjan, Aditya Joshi, Aakriti Singh, Mohd Shuaib, Manoj Diwakar, and Prabhishek Singh. "Video enabled crowd monitoring system and analysis." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915289.

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Sharmila, K. Soni, R.Thriveni, and T. Mukunda Priya. "Enhancing Crowd Image Analysis Through Facial Landmark Recognition." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10922068.

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Geçer, Melike, and Benoit Garbinato. "Tidal Crowds: A Federated Crowd Flow Prediction Algorithm." In ICGDA 2024: 2024 7th International Conference on Geoinformatics and Data Analysis. ACM, 2024. http://dx.doi.org/10.1145/3678599.3678609.

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De Luca, Antonio, Scott Lomax, and Marguerite Jeansonne Pinto. "Advanced analysis of a pedestrian bridge and considerations on crowd-structure interaction." In IABSE Symposium, Prague 2022: Challenges for Existing and Oncoming Structures. International Association for Bridge and Structural Engineering (IABSE), 2022. http://dx.doi.org/10.2749/prague.2022.1427.

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&lt;p&gt;Pedestrian bridges, grandstands, and other long-span structures may be subjected to crowd loading. Crowds have the dual effect to produce large forces and alter the structure’s modal properties and damping. Vibration testing of full-scale structures allows the verification of the modelling assumptions and design criteria.&lt;/p&gt;&lt;p&gt;This paper discusses the case study of a pedestrian bridge structure subjected to crowd loading. The results of the design finite element model of the bridge and the bridge performance were validated through field testing. The theoretical effects of
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Deshpande, N. P., and R. Gupta. "Crowd management using fuzzy logic and G.I.S." In RISK ANALYSIS 2010. WIT Press, 2010. http://dx.doi.org/10.2495/risk100281.

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Cao, Tian, Xinyu Wu, Jinnian Guo, Shiqi Yu, and Yangsheng Xu. "Abnormal crowd motion analysis." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420408.

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Mahata, Debanjan, and Nitin Agarwal. "Learning from the crowd." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. ACM, 2013. http://dx.doi.org/10.1145/2492517.2492661.

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Dupont, Camille, Luis Tobias, and Bertrand Luvison. "Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis." In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.271.

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Kishore, P. V. V., R. Rahul, K. Sravya, and A. S. C. S. Sastry. "Crowd Density Analysis and tracking." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275777.

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Reports on the topic "Analysis crowd"

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Petty, Mikel D., Ryland C. Gaskins, and Frederic D. McKenzie. Crowd Modeling in Military Simulations: Requirements Analysis, Survey, and Design Study. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada474641.

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Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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Conery, Ian, Brittany Bruder, Connor Geis, Jessamin Straub, Nicholas Spore, and Katherine Brodie. Applicability of CoastSnap, a crowd-sourced coastal monitoring approach for US Army Corps of Engineers district use. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47568.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details the pilot deployment, accuracy evaluation, and best practices of the citizen-science, coastal-image monitoring program CoastSnap. Despite the need for regular observational data, many coastlines are monitored infrequently due to cost and personnel, and this cell phone-image-based approach represents a new potential data source to districts in addition to providing an outreach opportunity for the public. Requiring minimal hardware and signage, the system is simple to install but re
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Drury, J., S. Arias, T. Au-Yeung, et al. Public behaviour in response to perceived hostile threats: an evidence base and guide for practitioners and policymakers. University of Sussex, 2023. http://dx.doi.org/10.20919/vjvt7448.

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Background: Public behaviour and the new hostile threats • Civil contingencies planning and preparedness for hostile threats requires accurate and up to date knowledge about how the public might behave in relation to such incidents. Inaccurate understandings of public behaviour can lead to dangerous and counterproductive practices and policies. • There is consistent evidence across both hostile threats and other kinds of emergencies and disasters that significant numbers of those affected give each other support, cooperate, and otherwise interact socially within the incident itself. • In emerg
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Anilkumar, Rahul, Benjamin Melone, Michael Patsula, et al. Canadian jobs amid a pandemic : examining the relationship between professional industry and salary to regional key performance indicators. Department of Systems and Computer Engineering, Carleton University, 2022. http://dx.doi.org/10.22215/dsce/220608.

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The COVID-19 pandemic has contributed to massive rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a “snapshot” of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indic
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Schomaker, Michael E., Stanley J. Zarnoch, William A. Bechtold, David J. Latelle, William G. Burkman, and Susan M. Cox. Crown-condition classification: a guide to data collection and analysis. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-gtr-102.

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Thompson, Peter, Håkan Frantzich, Silvia Arias, and Jesper Friholm. Determining Evacuation Capability with Biomechanical Data. SFPE Foundation, 2020. https://doi.org/10.64167/k24s-qsb5.

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Experimental data on single file pedestrian movement has been analysed and implemented in a prototype movement model. The prototype model is developed to predict movement of persons based on a first principle approach using basic population data such as age, height, gender and response time to adapt the walking speed in a crowd. The experimental data provide the biomechanical information needed in the model. The intention with the new approach is to present a predictive capability for the future as a consequence of the identified demographical changes observed in today's society.
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Cavallo, Eduardo A., and Christian Daude. Public Investment in Developing Countries: A Blessing or a Curse? Inter-American Development Bank, 2008. http://dx.doi.org/10.18235/0010897.

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This paper analyzes the impact of public investment on private investment in 116 developing countries between 1980 and 2006. It finds a strong crowding-out effect that seems to be the norm rather than the exception, both across regions and over time. This effect is dampened (or even reversed) in countries with better institutions that are more open to international trade and financial flows. These results confirm that while public infrastructure may complement private capital, distortions associated with the public investment process might crowd out private investment in the course of building
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Randolph, KaDonna C. Descriptive statistics of tree crown condition in the Southern United States and impacts on data analysis and interpretation. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2006. http://dx.doi.org/10.2737/srs-gtr-94.

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Randolph, KaDonna C. Descriptive statistics of tree crown condition in the Southern United States and impacts on data analysis and interpretation. U.S. Department of Agriculture, Forest Service, Southern Research Station, 2006. http://dx.doi.org/10.2737/srs-gtr-94.

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