CROWD VIDEO SEQUENCES PROCESSING METHODS FOR DETERMINING THE CROWD MOTION PATTERNS

Article Sidebar

Abstract views: 19
PDF Downloads: 8
pdf (rus) (Русский)
Published: Apr 24, 2024
DOI: https://doi.org/10.52928/2070-1624-2024-42-1-26-33
Keywords:
dynamic object, crowd, optical flow, objects cluster motion, motion maps динамический объект, скопление людей, оптический поток, движение скопления объектов, карты движения

Main Article Content

S. SHOLTANYUK
Belarusian State University, Minsk
Q. BU
Nanjing Research Institute of Electronics Engineering, China
A. NEDZVED
Belarusian State University, Minsk; United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk

Abstract

Nowadays, homogeneous objects clusters motion is one of the most important and rapidly developing computer vision and machine learning application. In this paper, we consider the crowd motion patterns determination by using motion maps that we calculate with FlowNet, a neural network examining motion of objects in a video sequence. This approach allows us to get information on the crowd direction and velocity with relation to other objects of scene, which plays the key role in behavior analysis and security establishment. Besides, we consider methods for preliminary video sequence processing, including frame combination, to estimate motion maps more precisely and improve the effectiveness of the dynamic scenes analysis.

Article Details

How to Cite
SHOLTANYUK, S., BU, Q., & NEDZVED, A. (2024). CROWD VIDEO SEQUENCES PROCESSING METHODS FOR DETERMINING THE CROWD MOTION PATTERNS. Vestnik of Polotsk State University. Part C. Fundamental Sciences, (1), 26-33. https://doi.org/10.52928/2070-1624-2024-42-1-26-33
Issue
No. 1 (2024)
Section
Информатика, вычислительная техника и управление
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biography

A. NEDZVED, Belarusian State University, Minsk; United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Minsk

д-р техн. наук, доц.

References

Ascione, F., Caserta, S., Perris, R., & Guidoa, S. (2014). Investigation of Cell Dynamics in vitro by Time Lapse Microscopy and Image Analysis. Chemical Engineering, (38), 517–522. DOI: 10.3303/CET1438087.

Chen C., Ye S., Chen H., Nedzvedz, O. V., & Ablameyko, S. V. (2017). Integral Optical Flow and its Application for Monitoring Dynamic Objects from a Video Sequence. J. of Applied Spectroscopy, (84), 120–128. DOI: 10.1007/s10812-017-0437-z.

Sholtanyuk, S. (2023). Crowd Abnormal Behaviour Patterns: Survey and Detection. Central European Researchers Journal, 9(1), 48-58. https://ceres-journal.eu/download.php?file=2023_01_7.pdf.

Altowairqi, S., Luo, S., & Greer, P. (2023). A Review of the Recent Progress on Crowd Anomaly Detection. Intern. J. of Advanced Computer Science and Applications, 14(4), 659–669. DOI: 10.14569/IJACSA.2023.0140472.

Choudhry, N., Abawajy, J., Huda, S., & Rao, I. (2023). A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection. IEEE Access, (11), 114680–114713. DOI: 10.1109/ACCESS.2023.3321800.

Miao, Y., Yang, J., Alzahrani, B., Lv, G., Alafif, T., Barnawi, A., & Chen, M. (2022). Abnormal Behavior Learning Based on Edge Computing toward a Crowd Monitoring System. IEEE Network, 36(3), 90–96. DOI: 10.1109/MNET.014.2000523.

Alafif, T., Alzahrani, B., Cao, Y., Alotaibi, R., Barnawi, A., & Chen, M. (2022). Generative adversarial network based abnormal behavior detection in massive crowd videos: a Hajj case study. J. of Ambient Intelligence and Humanized Computing, 13(8), 4077–4088. DOI: 10.1007/s12652-021-03323-5.

Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017). Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (2462–2470). IEEE. DOI: 10.1109/CVPR.2017.179.

Chertkov, V. M., & Zheleznjak, V. K. (2018). Algoritm opredelenija mery shozhesti identifikacionnyh obrazov zakladnyh ustrojstv [Algorithm for Determining the Degree of Similarity of Identification Images from Secret Intelligence Device]. Vestnik Polotskogo gosudarstvennogo universiteta. Seriya C, Fundamental'nye nauki [Herald of Polotsk State University. Series С. Fundamental sciences], (4), 20–27. (In Russ., abstr. in Engl.).

Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. DOI: 10.1145/358669.358692.

Liu, C., Xu, J., & Wang, F. (2021). A review of keypoints’ detection and feature description in image registration. Scientific programming, (2021), 8509164:1–8509164:25. DOI: 10.1155/2021/8509164.

Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In 2011 Intern. Conf. on Computer Vision (2564–2571). IEEE. DOI: 10.1109/ICCV.2011.6126544.

Andersson, O., & Reyna Marquez, S. (2016). A comparison of object detection algorithms using unmanipulated testing images: Comparing SIFT, KAZE, AKAZE and ORB. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A927480&dswid=9533.