Academic literature on the topic 'Crowd dataset'
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Journal articles on the topic "Crowd dataset"
Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Wan Noorshahida Mohd Isa, Jia Uddin, and Norra Abdullah. "Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage." Sensors 22, no. 14 (July 7, 2022): 5102. http://dx.doi.org/10.3390/s22145102.
Full textBhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Mohammad Ahsanul Haque, Jia Uddin, Wan Noorshahida Mohd Isa, Mohd Nizam Husen, and Norra Abdullah. "A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network." PeerJ Computer Science 8 (March 25, 2022): e895. http://dx.doi.org/10.7717/peerj-cs.895.
Full textAlafif, Tarik, Anas Hadi, Manal Allahyani, Bander Alzahrani, Areej Alhothali, Reem Alotaibi, and Ahmed Barnawi. "Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds." Electronics 12, no. 5 (February 28, 2023): 1165. http://dx.doi.org/10.3390/electronics12051165.
Full textRen, Guoyin, Xiaoqi Lu, and Yuhao Li. "Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis." Journal of Sensors 2022 (August 12, 2022): 1–19. http://dx.doi.org/10.1155/2022/7247757.
Full textBHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah, and Dr Mohd Ali Samsudin. "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.
Full textBHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah, and Dr Mohd Ali Samsudin. "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.
Full textWu, Junfeng, Zhiyang Li, Wenyu Qu, and Yizhi Zhou. "One Shot Crowd Counting with Deep Scale Adaptive Neural Network." Electronics 8, no. 6 (June 21, 2019): 701. http://dx.doi.org/10.3390/electronics8060701.
Full textKaya, Abdil, Stijn Denis, Ben Bellekens, Maarten Weyn, and Rafael Berkvens. "Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation." Data 5, no. 2 (June 9, 2020): 52. http://dx.doi.org/10.3390/data5020052.
Full textShao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang, and Huayi Zhan. "A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV." Sensors 20, no. 19 (September 28, 2020): 5550. http://dx.doi.org/10.3390/s20195550.
Full textZhang, Cong, Kai Kang, Hongsheng Li, Xiaogang Wang, Rong Xie, and Xiaokang Yang. "Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset." IEEE Transactions on Multimedia 18, no. 6 (June 2016): 1048–61. http://dx.doi.org/10.1109/tmm.2016.2542585.
Full textDissertations / Theses on the topic "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.
Full textMaster of Science
CONIGLIARO, Davide. "Spectator crowd: a social signal processing perspective." Doctoral thesis, 2016. http://hdl.handle.net/11562/940037.
Full textWhat 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.
Book chapters on the topic "Crowd dataset"
Aksoy, Yağız, Changil Kim, Petr Kellnhofer, Sylvain Paris, Mohamed Elgharib, Marc Pollefeys, and Wojciech Matusik. "A Dataset of Flash and Ambient Illumination Pairs from the Crowd." In Computer Vision – ECCV 2018, 644–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_39.
Full textCychnerski, Jan, and Tomasz Dziubich. "Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations." In 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.
Full textRizvi, Syed Zeeshan, Muhammad Umar Farooq, and Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting." In Digital Interaction and Machine Intelligence, 75–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.
Full textFavaretto, Rodolfo Migon, Soraia Raupp Musse, and Angelo Brandelli Costa. "Video Analysis Dataset and Applications." In 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.
Full textJingying, Wang. "A Survey on Crowd Counting Methods and Datasets." In 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.
Full textPohlisch, Jakob. "Managing the Crowd: A Literature Review of Empirical Studies on Internal Crowdsourcing." In Contributions to Management Science, 27–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52881-2_3.
Full textAlameda-Pineda, Xavier, Ramanathan Subramanian, Elisa Ricci, Oswald Lanz, and Nicu Sebe. "SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions." In Group and Crowd Behavior for Computer Vision, 321–40. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-809276-7.00017-5.
Full textter Veen, James, Shahram Sarkani, and Thomas A. Mazzuchi. "Seeking an Online Social Media Radar." In 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.
Full textZhong, Wencan, Vijayalakshmi G. V. Mahesh, Alex Noel Joseph Raj, and Nersisson Ruban. "Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks." In 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.
Full textZhang, Hongyu, and Jacek Malczewski. "Quality Evaluation of Volunteered Geographic Information." In Crowdsourcing, 1173–201. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8362-2.ch058.
Full textConference papers on the topic "Crowd dataset"
Mahmoud, Samar, and Yasmine Arafaf. "Abnormal High-Density Crowd Dataset." In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264277.
Full textDupont, 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.
Full textSchmuck, Viktor, and Oya Celiktutan. "RICA: Robocentric Indoor Crowd Analysis Dataset." In UKRAS20 Conference: “Robots into the real world”. EPSRC UK-RAS Network, 2020. http://dx.doi.org/10.31256/io1sq2r.
Full textHuang, Hun, Ge Gao, Ziyi Ke, Cheng Peng, and Ming Gu. "A Multi-Scenario Crowd Data Synthesis Based On Building Information Modeling." In The 29th EG-ICE International Workshop on Intelligent Computing in Engineering. EG-ICE, 2022. http://dx.doi.org/10.7146/aul.455.c223.
Full textSohn, Samuel S., Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic, and Mubbasir Kapadia. "Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction." In 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.
Full textKiskin, Ivan, Adam D. Cobb, Lawrence Wang, and Stephen Roberts. "Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053141.
Full textSchroder, Gregory, Tobias Senst, Erik Bochinski, and Thomas Sikora. "Optical Flow Dataset and Benchmark for Visual Crowd Analysis." In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018. http://dx.doi.org/10.1109/avss.2018.8639113.
Full textKwon, Heeyoung, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, and Niranjan Balasubramanian. "Modeling Preconditions in Text with a Crowd-sourced Dataset." In 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.
Full textMa, Zhiheng, Xiaopeng Hong, Xing Wei, Yunfeng Qiu, and Yihong Gong. "Towards A Universal Model for Cross-Dataset Crowd Counting." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00319.
Full textPan, Zhiwen, Shuangye Zhao, Jesus Pacheco, Yuxin Zhang, Xiaofan Song, Yiqiang Chen, Lianjun Dai, and Jun Zhang. "Comprehensive Data Management and Analytics for General Society Survey Dataset." In 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.
Full textReports on the topic "Crowd dataset"
Anilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil, and 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, June 2022. http://dx.doi.org/10.22215/dsce/220608.
Full textLu, Tianjun, Jian-yu Ke, Fynnwin Prager, and 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, August 2022. http://dx.doi.org/10.31979/mti.2022.2147.
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