Motion estimation of high density crowd using fluid dynamics-Reference-Cited by-同舟云学术

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Motion estimation of high density crowd using fluid dynamics

Published:2020-04-02 Issue:3 Volume:68 Page:141-155
ISSN:1368-2199
Container-title:The Imaging Science Journal
language:en
Short-container-title:The Imaging Science Journal

Author:

Farooq Muhammad Umer¹,Saad Mohamed Naufal B. M.¹,Malik Aamir Saeed¹,Salih Ali Yasir²,Khan Sultan Daud³

Affiliation:

1. Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Perak Darul Rizwan, Malaysia

2. Electircal Engineering Department, Faculty of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Saudi Arabia

3. College of Computer Science & Software Engineering, University of Hail, Mekkah, Saudi Arabia

Publisher

Informa UK Limited

Subject

Computer Vision and Pattern Recognition,Media Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/13682199.2020.1767843

Reference85 articles.

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2. Yamamoto M, Ohta T, Yamagiwa T, et al. Human action tracking guided by key-frames. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580); 2000; Grenoble, France. p. 354–361. doi:10.1109/AFGR.2000.840659.

3. Duan S, Wang X, Yu X. Crowded abnormal detection based on mixture of kernel dynamic texture. 2014 International Conference on Audio, Language and Image Processing; 2014; Shanghai. p. 931–936. doi:10.1109/ICALIP.2014.7009931.

4. Xu J, Denman S, Sridharan S, et al. Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes. Proceedings of the 2011 joint ACM workshop on Modeling and representing events (J-MRE '11). New York (NY): Association for Computing Machinery; 2011. p. 25–30. doi:10.1145/2072508.2072515.

5. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models

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