A Framework for the Video Surveillance Suspicious Activity Detection
Received: 12 April 2025 | Revised: 17 May 2025 and 28 May 2025 | Accepted: 7 June 2025 | Online: 2 August 2025
Corresponding author: Shashank Dhananjaya
Abstract
Video surveillance is globally considered to be of considerable importance. Recent advances have resulted in notable improvements in the incorporation of artificial intelligence, machine learning, and deep learning techniques into video surveillance devices. The utilization of combinations and distinct frameworks facilitates the differentiation of various questionable behaviors through real-time image analysis. Human behavior is inherently unpredictable, making it difficult to determine whether it is suspicious or typical. This study characterized human actions into two categories: normal and suspicious. Normal actions include sitting, strolling, running, waving hands, etc., while arrest, abuse, shoplifting, etc., are examples of suspicious actions. This study used a convolutional neural network, achieving 97.96% accuracy on the CIFAR-100 dataset, demonstrating its effectiveness in recognizing and categorizing various activities, and paving the way for improved surveillance and security applications. Future work will focus on further refining the model and expanding its capabilities to include real-time video analysis, allowing more dynamic responses to potential threats and enabling faster decision-making in critical situations. Additionally, the integration of advanced algorithms for behavior prediction could further enhance the model's performance in complex environments.
Keywords:
profound learning, convolutional neural networks, suspicious activity detectionDownloads
References
K. Ouivirach, S. Gharti, and M. N. Dailey, "Incremental behavior modeling and suspicious activity detection," Pattern Recognition, vol. 46, no. 3, Mar. 2013. DOI: https://doi.org/10.1016/j.patcog.2012.10.008
X. Zhang, S. Yang, X. Zhang, W. Zhang, and J. Zhang, "Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning." arXiv, May 27, 2018.
R. K. Tripathi, A. S. Jalal, and S. C. Agrawal, "Suspicious human activity recognition: a review," Artificial Intelligence Review, vol. 50, no. 2, pp. 283–339, Aug. 2018. DOI: https://doi.org/10.1007/s10462-017-9545-7
F. G. I. Salem, R. Hassanpour, A. A. Ahmed, and A. Douma, "Detection of Suspicious Activities of Human from Surveillance Videos," in 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, Tripoli, Libya, May 2021, pp. 794–801. DOI: https://doi.org/10.1109/MI-STA52233.2021.9464477
T. A. Mostafa, J. Uddin, and Md. H. Ali, "Abnormal event detection in crowded scenarios," in 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Dec. 2017, pp. 1–6. DOI: https://doi.org/10.1109/EICT.2017.8275217
D. Xu, Y. Yan, E. Ricci, and N. Sebe, "Detecting anomalous events in videos by learning deep representations of appearance and motion," Computer Vision and Image Understanding, vol. 156, pp. 117–127, Mar. 2017. DOI: https://doi.org/10.1016/j.cviu.2016.10.010
"DCSASS Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/mateohervas/dcsass-dataset.
N. Gupta and B. B. Agarwal, "Recognition of Suspicious Human Activity in Video Surveillance: A Review," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10529–10534, Apr. 2023. DOI: https://doi.org/10.48084/etasr.5739
M. Karim, S. Khalid, A. Aleryani, N. Tairan, Z. Ali, and F. Ali, "HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods," IEEE Access, vol. 12, pp. 42769–42790, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3378515
M. Karim, S. Khalid, A. Aleryani, J. Khan, I. Ullah, and Z. Ali, "Human Action Recognition Systems: A Review of the Trends and State-of-the-Art," IEEE Access, vol. 12, pp. 36372–36390, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3373199
S. Khalid, S. Wu, A. Alam, and I. Ullah, "Real-time feedback query expansion technique for supporting scholarly search using citation network analysis," Journal of Information Science, vol. 47, no. 1, pp. 3–15, Feb. 2021. DOI: https://doi.org/10.1177/0165551519863346
E. Yilmaz and O. Can, "Unveiling Shadows: Harnessing Artificial Intelligence for Insider Threat Detection," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13341–13346, Apr. 2024. DOI: https://doi.org/10.48084/etasr.6911
Q. U. A. Arshad et al., "Anomalous Situations Recognition in Surveillance Images Using Deep Learning," Computers, Materials and Continua, vol. 76, no. 1, pp. 1103–1125, Jun. 2023. DOI: https://doi.org/10.32604/cmc.2023.039752
S. Kale and R. Shriram, "Suspicious Activity Detection Using Transfer Learning Based ResNet Tracking from Surveillance Videos," in Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), 2021, pp. 208–220. DOI: https://doi.org/10.1007/978-3-030-73689-7_21
S. Vorapatratorn, "Enhancing monitoring of suspicious activities with AI-based and big data fusion," PeerJ Computer Science, vol. 10, Jan. 2024, Art. no. e1741. DOI: https://doi.org/10.7717/peerj-cs.1741
C. V. Amrutha, C. Jyotsna, and J. Amudha, "Deep Learning Approach for Suspicious Activity Detection from Surveillance Video," in 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, Mar. 2020, pp. 335–339. DOI: https://doi.org/10.1109/ICIMIA48430.2020.9074920
A. Krizhevsky, "Learning Multiple Layers of Features from Tiny Images," M.S. Thesis, University of Toronto, Canada, 2009.
T. Saba, A. Rehman, R. Latif, S. M. Fati, M. Raza, and M. Sharif, "Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization," IEEE Access, vol. 9, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3091081
B. A. Alavudeen, P. Parthasarathy, and S. Vivekanandan, "Detection of Suspicious Human Activity based on CNN-DBNN Algorithm for Video Surveillance Applications," in 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, Mar. 2019, pp. 1–7. DOI: https://doi.org/10.1109/i-PACT44901.2019.8960085
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Copyright (c) 2025 K. Rohitaksha, Annapurna L. Pujari, Shashank Dhananjaya, M. Narender
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