Littérature scientifique sur le sujet « Crowd dataset »
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Articles de revues sur le sujet "Crowd dataset"
Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Wan Noorshahida Mohd Isa, Jia Uddin et Norra Abdullah. « Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage ». Sensors 22, no 14 (7 juillet 2022) : 5102. http://dx.doi.org/10.3390/s22145102.
Texte intégralBhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Mohammad Ahsanul Haque, Jia Uddin, Wan Noorshahida Mohd Isa, Mohd Nizam Husen et Norra Abdullah. « A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network ». PeerJ Computer Science 8 (25 mars 2022) : e895. http://dx.doi.org/10.7717/peerj-cs.895.
Texte intégralAlafif, Tarik, Anas Hadi, Manal Allahyani, Bander Alzahrani, Areej Alhothali, Reem Alotaibi et Ahmed Barnawi. « Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds ». Electronics 12, no 5 (28 février 2023) : 1165. http://dx.doi.org/10.3390/electronics12051165.
Texte intégralRen, Guoyin, Xiaoqi Lu et Yuhao Li. « Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis ». Journal of Sensors 2022 (12 août 2022) : 1–19. http://dx.doi.org/10.1155/2022/7247757.
Texte intégralBHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah et Dr Mohd Ali Samsudin. « Crowd density estimation using deep learning for Hajj pilgrimage video analytics ». F1000Research 10 (14 janvier 2022) : 1190. http://dx.doi.org/10.12688/f1000research.73156.2.
Texte intégralBHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah et Dr Mohd Ali Samsudin. « Crowd density estimation using deep learning for Hajj pilgrimage video analytics ». F1000Research 10 (24 novembre 2021) : 1190. http://dx.doi.org/10.12688/f1000research.73156.1.
Texte intégralWu, Junfeng, Zhiyang Li, Wenyu Qu et Yizhi Zhou. « One Shot Crowd Counting with Deep Scale Adaptive Neural Network ». Electronics 8, no 6 (21 juin 2019) : 701. http://dx.doi.org/10.3390/electronics8060701.
Texte intégralKaya, Abdil, Stijn Denis, Ben Bellekens, Maarten Weyn et Rafael Berkvens. « Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation ». Data 5, no 2 (9 juin 2020) : 52. http://dx.doi.org/10.3390/data5020052.
Texte intégralShao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang et Huayi Zhan. « A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV ». Sensors 20, no 19 (28 septembre 2020) : 5550. http://dx.doi.org/10.3390/s20195550.
Texte intégralZhang, Cong, Kai Kang, Hongsheng Li, Xiaogang Wang, Rong Xie et Xiaokang Yang. « Data-Driven Crowd Understanding : A Baseline for a Large-Scale Crowd Dataset ». IEEE Transactions on Multimedia 18, no 6 (juin 2016) : 1048–61. http://dx.doi.org/10.1109/tmm.2016.2542585.
Texte intégralThèses sur le sujet "Crowd dataset"
Choudhury, Ananya. « WiSDM : a platform for crowd-sourced data acquisition, analytics, and synthetic data generation ». Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72256.
Texte intégralMaster of Science
CONIGLIARO, Davide. « Spectator crowd : a social signal processing perspective ». Doctoral thesis, 2016. http://hdl.handle.net/11562/940037.
Texte intégralWhat this thesis proposes is a new type of crowd analysis in computer vision, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see". Typical scenarios of spectator crowds are stadiums, amphitheaters, classrooms, etc., and they share some aspects with classical crowd monitoring; for instance, since many people are simultaneously observed, per-person analysis is hard; however, in the considered cases, the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, watch, support/heckle the players or discuss with the neighbors. We start facing this challenge by following a social signal processing approach, which grounds computer vision techniques in social theories. More specifically, leveraging on social theories describing expressive bodily conduct, we will show interesting results on how it is possible to distinguish people behaviors by automatically detecting their social activities. In particular, we propose a novel dataset, the Spectators Hockey (S-Hock), which deals with 4 hockey matches recorded during an international tournament. A massive annotation has been carried out on the dataset, focusing on the spectators at different levels of detail: at a higher level, people have been labeled depending on the team they were supporting and on the acquaintance they have with spectators who sit close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body), but also fine grained actions such as hands on hips, clapping hands, etc. The labeling has also been focused on the game field, allowing to relate what was going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard lowlevel applications as object counting, people detection and head pose estimation, but also for high-level tasks, as spectator categorization and event recognition. For all of these we provide protocols and baseline results, encouraging further research. In this general picture, this thesis has been devoted to demonstrate that a strong sociological background is necessary to deal with crowd analysis in general, but also to underline the need to explore a novel specific issue, namely spectator crowd, by developing approaches able to adapt to the peculiarities of this scenario, which is new in computer vision. We are confident that S-Hock and our studies may trigger the design of novel and effective approaches for the analysis of human behavior in crowded settings and environments.
Chapitres de livres sur le sujet "Crowd dataset"
Aksoy, Yağız, Changil Kim, Petr Kellnhofer, Sylvain Paris, Mohamed Elgharib, Marc Pollefeys et Wojciech Matusik. « A Dataset of Flash and Ambient Illumination Pairs from the Crowd ». Dans Computer Vision – ECCV 2018, 644–60. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_39.
Texte intégralCychnerski, Jan, et Tomasz Dziubich. « Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations ». Dans New Trends in Database and Information Systems, 205–16. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85082-1_19.
Texte intégralRizvi, Syed Zeeshan, Muhammad Umar Farooq et Rana Hammad Raza. « Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images : Case Study of Crowd Counting ». Dans Digital Interaction and Machine Intelligence, 75–87. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.
Texte intégralFavaretto, Rodolfo Migon, Soraia Raupp Musse et Angelo Brandelli Costa. « Video Analysis Dataset and Applications ». Dans Emotion, Personality and Cultural Aspects in Crowds, 127–39. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22078-5_10.
Texte intégralJingying, Wang. « A Survey on Crowd Counting Methods and Datasets ». Dans Advances in Computer, Communication and Computational Sciences, 851–63. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4409-5_76.
Texte intégralPohlisch, Jakob. « Managing the Crowd : A Literature Review of Empirical Studies on Internal Crowdsourcing ». Dans Contributions to Management Science, 27–53. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52881-2_3.
Texte intégralAlameda-Pineda, Xavier, Ramanathan Subramanian, Elisa Ricci, Oswald Lanz et Nicu Sebe. « SALSA : A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions ». Dans Group and Crowd Behavior for Computer Vision, 321–40. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-809276-7.00017-5.
Texte intégralter Veen, James, Shahram Sarkani et Thomas A. Mazzuchi. « Seeking an Online Social Media Radar ». Dans Social Media and the Transformation of Interaction in Society, 67–92. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8556-7.ch005.
Texte intégralZhong, Wencan, Vijayalakshmi G. V. Mahesh, Alex Noel Joseph Raj et Nersisson Ruban. « Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks ». Dans Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, 241–65. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6690-9.ch013.
Texte intégralZhang, Hongyu, et Jacek Malczewski. « Quality Evaluation of Volunteered Geographic Information ». Dans Crowdsourcing, 1173–201. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8362-2.ch058.
Texte intégralActes de conférences sur le sujet "Crowd dataset"
Mahmoud, Samar, et Yasmine Arafaf. « Abnormal High-Density Crowd Dataset ». Dans 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264277.
Texte intégralDupont, Camille, Luis Tobias et Bertrand Luvison. « Crowd-11 : A Dataset for Fine Grained Crowd Behaviour Analysis ». Dans 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.271.
Texte intégralSchmuck, Viktor, et Oya Celiktutan. « RICA : Robocentric Indoor Crowd Analysis Dataset ». Dans UKRAS20 Conference : “Robots into the real world”. EPSRC UK-RAS Network, 2020. http://dx.doi.org/10.31256/io1sq2r.
Texte intégralHuang, Hun, Ge Gao, Ziyi Ke, Cheng Peng et Ming Gu. « A Multi-Scenario Crowd Data Synthesis Based On Building Information Modeling ». Dans The 29th EG-ICE International Workshop on Intelligent Computing in Engineering. EG-ICE, 2022. http://dx.doi.org/10.7146/aul.455.c223.
Texte intégralSohn, Samuel S., Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic et Mubbasir Kapadia. « Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/185.
Texte intégralKiskin, Ivan, Adam D. Cobb, Lawrence Wang et Stephen Roberts. « Humbug Zooniverse : A Crowd-Sourced Acoustic Mosquito Dataset ». Dans ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053141.
Texte intégralSchroder, Gregory, Tobias Senst, Erik Bochinski et Thomas Sikora. « Optical Flow Dataset and Benchmark for Visual Crowd Analysis ». Dans 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018. http://dx.doi.org/10.1109/avss.2018.8639113.
Texte intégralKwon, Heeyoung, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers et Niranjan Balasubramanian. « Modeling Preconditions in Text with a Crowd-sourced Dataset ». Dans Findings of the Association for Computational Linguistics : EMNLP 2020. Stroudsburg, PA, USA : Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.340.
Texte intégralMa, Zhiheng, Xiaopeng Hong, Xing Wei, Yunfeng Qiu et Yihong Gong. « Towards A Universal Model for Cross-Dataset Crowd Counting ». Dans 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00319.
Texte intégralPan, Zhiwen, Shuangye Zhao, Jesus Pacheco, Yuxin Zhang, Xiaofan Song, Yiqiang Chen, Lianjun Dai et Jun Zhang. « Comprehensive Data Management and Analytics for General Society Survey Dataset ». Dans ICCSE'19 : The 4th International Conference on Crowd Science and Engineering. New York, NY, USA : ACM, 2019. http://dx.doi.org/10.1145/3371238.3371269.
Texte intégralRapports d'organisations sur le sujet "Crowd dataset"
Anilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil et G. A. Wainer. 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, juin 2022. http://dx.doi.org/10.22215/dsce/220608.
Texte intégralLu, Tianjun, Jian-yu Ke, Fynnwin Prager et Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond : Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, août 2022. http://dx.doi.org/10.31979/mti.2022.2147.
Texte intégral