Anomaly Detection on Video by Detecting and Tracking Feature Points-Reference-Cited by-同舟云学术

Anomaly Detection on Video by Detecting and Tracking Feature Points

Published:2023-06-19 Issue: Volume: Page:
ISSN:
Container-title:INTELS’22
language:
Short-container-title:

Author:

Fomin Ivan¹^ORCID,Rezets Yurii¹,Smirnova Ekaterina¹^ORCID

Affiliation:

1. Russian State Scientific Center for Robotics and Technical Cybernetics (RTC), Tikhoretsky Prospect, 21, St. Petersburg 194064, Russia

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/33/1/34/pdf

Reference39 articles.

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3. Mehran, R., Oyama, A., and Shah, M. (2009, January 20–25). Abnormal crowd behavior detection using social force model. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.

4. Zhou, B., Wang, X., and Tang, X. (2012, January 16–21). Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.

5. Ravanbakhsh, M., Mousavi, H., Nabi, M., Marcenaro, L., and Regazzoni, C. (2018, January 27–30). Fast but not deep: Efficient crowd abnormality detection with local binary tracklets. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.