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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Optimizing the deep features using the spectral concept for an efficient video classification

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pp. 553–562Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1959XML
Received:
07 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1959
Pages:
553–562

Abstract

The identification of suspicious activity is greatly influenced by the efficient learning and classification of video data. Learning large videos requires more processing time than conventional deep learning methods, because deep features have relatively large dimensions. In the present investigation, a spectral-based deep learning method for producing reduced or lower dimensional representations of surveillance videos is developed. There are two essential steps in the proposed method. In order to identify suspicious activity, the first method involves transforming the deep characteristics of video data into lower-manifold spectral space using classification techniques. The lower-rank representation of the video data is found by computing a Laplacian matrix. To show the effectiveness of the suggested strategy in comparison to current methods, experiments are conducted on benchmarked video surveillance datasets.

Keywords

Subject Classifications

68P3068Q30

References

[1] E. Şengönül, R. Samet, Q. Abu Al-Haija, A. Alqahtani, B. Alturki, and A. Alsulami, “An Analysis of Artificial Intelligence Techniques in Surveillance Video Anomaly Detection: A Comprehensive Survey,” Appl. Sci., vol. 13, no. 8, p. 4956 (2023). [Online] Available: https://doi.org/10.3390/app13084956.
[2] R. Savran Kızıltepe, J. Q. Gan, and J. J. Escobar, “A Novel Keyframe Extraction Method for Video Classification Using Deep Neural Networks,” Neural Comput & Applic, vol. 35, pp. 24513–24524 (2023). [Online] Available: https://doi.org/10.1007/s00521-021-06322-x.
[3] N. A. Shelke and S. S. Kasana, “Multiple Forgery Detection in Digital Video with VGG-16-Based Deep Neural Network and KPCA,” Multimed. Tools Appl., vol. 83, pp. 5415–5435 (2024). [Online] Available: https://doi.org/10.1007/s11042-023-15561-0.
[4] N. Nallappan and R. Velswamy, “Exploring Deep Learning-Based Content-Based Video Retrieval with Hierarchical Navigable Small World Index and ResNet-50 Features for Anomaly Detection,” Expert Syst. Appl., vol. 247 (2024), [Online] Available: https://doi.org/10.1016/j.eswa.2024.123197.
[5] B. Li, Y. Karaca, D. Baleanu, Y. D. Zhang, O. Gervasi, and M. Moonis, “Facial Expression Recognition by DenseNet-121,” in Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems, Academic Press (2022). [Online] Available: https://doi.org/10.1016/B978-0-323-90032-4.00019-5.
[6] L. Liu, X. Wang, Q. Bao, and X. Li, “Behavior Detection and Evaluation Based on Multi-frame MobileNet,” Multimed. Tools Appl., vol. 83, pp. 15733–15750 (2024). [Online] Available: https://doi.org/10.1007/s11042-023-16150-x.
[7] V. Singh, A. Baral, R. Kumar, S. Tummala, M. Noori, S.V. Yadav, S. Kang, and W. Zhao, “A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms,” Sensors (2024). [Online] Available: https://doi.org/10.3390/s24227249.
[8] Z. Mutalova, A. Shaushenova, A. Nurpeisova, M. Ongarbayeva, A. Ispussinov, S. Bekenova, and Z. Altynbekova, “Development of a Mathematical Model for Detecting Moving Objects in Video Streams in Real-Time,” IEEE Access, vol. 12, pp. 169235–169246 (2024). [Online] Available: https://doi.org/10.1109/ACCESS.2024.3487783.
[9] S. Rajagopal, M. Uma Devi, G. Maria Jones, and M. Gomathy Nayagam, “Ensemble Random Forest-Based Gradient Optimization Based Energy Efficient Video Processing System for Smart Traffic Surveillance System,” IETE J. Res., vol. 70, no. 9, pp. 7175–7191 (2024). [Online] Available: https://doi.org/10.1080/03772063.2024.2350927.
[10] X. Wang, “Support Vector Machine-Based Video Anomaly Detection Approaches,” in Anomaly Detection in Video Surveillance. Cognitive Intelligence and Robotics, Springer, Singapore (2024). [Online] Available: https://doi.org/10.1007/978-981-97-3023-0_7.
[11] X. Wang, “K-Nearest Neighbor-Based Video Anomaly Detection Approaches,” in Anomaly Detection in Video Surveillance. Cognitive Intelligence and Robotics, Springer, Singapore (2024). [Online] Available: https://doi.org/10.1007/978-981-97-3023-0_4.
[12] N. D. Bird, O. Masoud, N. P. Papanikolopoulos, and A. Isaacs, “Detection of Loitering Individuals in Public Transportation Areas,” IEEE Trans. Intell. Transp. Syst., vol. 6, no. 2, pp. 167–177, Jun. (2005).
[13] N. Bird, S. Atev, N. Caramelli, R. Martin, O. Masoud, and N. P. Papanikolopoulos, “Real-Time, Online Detection of Abandoned Objects in Public Areas,” in Proc. IEEE ICRA, pp. 3775–3780 (2006).
[14] L. Sijun, Z. Jian, and D. Feng, “A Knowledge-Based Approach for Detecting Unattended Packages in Surveillance Video,” in Proc. IEEE AVSS, p. 110 (2006).
[15] S. Blunsden and R. B. Fisher, “The BEHAVE Video Dataset: Ground Truthed Video for Multi-Person Behavior Classification,” Annu. BMVA, vol. 2010, no. 4, pp. 1–11 (2010).
[16] S. Blunsden, E. Andrade, and R. Fisher, “Non-Parametric Classification of Human Interaction,” in Proc. 3rd Iberian Conf. Pattern Recog. Image Anal., Part II, Girona, Spain, pp. 347–354 (2007).
[17] M. Elhamod and M. D. Levine, “Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 2, pp. 688–699 (2013).
[18] Z. Lu and K. Grauman, “Story-Driven Summarization for Egocentric Video,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 2714–2721 , Jun. (2013).
[19] W. Wolf, “Key Frame Selection by Motion Analysis,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. Conf., pp. 1228–1231, May (1996).
[20] S. Wan, X. Xu, T. Wang, and Z. Gu, “An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4487–4495, Jul. (2021). [Online] Available: https://doi.org/10.1109/TITS.2020.3017505.
[21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proc. Adv. Neural Inf. Process. Syst., pp. 1097–1105 (2012).
[22] Y. S. Chong and Y. H. Tay, “Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder,” in Proc. Int. Symp. Neural Netw., Cham, Switzerland: Springer, pp. 189–196, Dec. (2017).
[23] W. Sultani, C. Chen, and M. Shah, “Real-World Anomaly Detection in Surveillance Videos,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 6479–6488 (2018).
[24] P. K. Mishra, A. Mihailidis, and S. S. Khan, “Skeletal Video Anomaly Detection Using Deep Learning: Survey, Challenges, and Future Directions,” IEEE Trans. Emerging Topics Comput. Intell., vol. 8, no. 2, pp. 1073–1085, Apr. (2024). [Online] Available: https://doi.org/10.1109/TETCI.2024.3358103.
[25] N. Janu, A. Kumar, L. Raja, V. Bhatnagar, A. Kumar, S. K. Ramesh, and C. Poonia, “Development of an Efficient Real-Time H.264/AVC Advanced Video Compression Encryption Scheme,” J. Discrete Math. Sci. Cryptogr., vol. 24, no. 8, pp. 2245–2255 (2022).
[26] G. Saini, S. Bhatnagar, L. Raja, S. Sharma, and R. C. Poonia, “Structural Equation-Based Model to Investigate the Moderating Effect of Fear of COVID Using Partial Least Square Method,” J. Interdiscip. Math., Taylor & Francis, 22 Feb (2022).

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