Recognizing Unusual Activity with the Deep Learning Perspective in Crowd Segment-Reference-Cited by-同舟云学术

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Recognizing Unusual Activity with the Deep Learning Perspective in Crowd Segment

Published:2021-08-24 Issue: Volume: Page:171-181
ISSN:1868-4394
Container-title:A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems
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Author:

Bala Bhoomeshwar,Kadurka Raja Shekar,Negasa Galeta

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-76653-5_9

Reference23 articles.

1. Weidmann, U. (1993). Transporttechnik der Fussgänger. Technical Report 90, ETH Zürich.

2. Cem, D., Melike, S., & Noel, E. (2017). Abnormal crowd behavior detection using novel optical flow-based features.

3. Hiba, H., & Sreela, S. (2013). Detection of abnormal behavior in dynamic crowded gatherings.

4. Mehran, R., Oyama, A., & Shah, M.: Abnormal crowd behavior detection using social force model.

5. Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51, 4282.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Human crowd behaviour analysis based on video segmentation and classification using expectation–maximization with deep learning architectures;Multimedia Tools and Applications;2024-03-16

2. Literature Review on Human Behavioural Analysis Using Deep Learning Algorithm;IFIP Advances in Information and Communication Technology;2022

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