Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 03 | Issue : 01 | Page : –
    By

    Prati Dubey,

  • Rakesh Kumar Mittan,

  1. Research Scholar, Department of Computer Science and Engineering, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India
  2. Associate Professor, Department of Computer Science and Engineering, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India

Abstract

In the current era of advanced security systems, video surveillance plays an essential role in ensuring safety by detecting suspicious activities. With the increase in real-time data, manual monitoring has become impractical, paving the way for automated surveillance systems utilizing machine learning (ML) and artificial intelligence (AI) technologies. This paper explores the integration of ML and AI models, specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, for suspicious human activity detection in video streams. The proposed system involves video data collection, preprocessing, feature extraction, and model training to identify abnormal behaviour. We review existing literature on human activity detection, discussing several models and techniques for video anomaly detection and object tracking. The study demonstrates how these intelligent systems can be applied in various environments, providing real-time insights and proactive security measures. The paper also highlights the challenges related to deep learning, such as overfitting, computational requirements, and the need for extensive labelled data for effective model training.

Keywords: Suspicious activity detection, video surveillance, machine learning, convolutional neural networks (CNN), long short-term memory (LSTM) networks

[This article belongs to International Journal of Optical Innovations & Research ]

How to cite this article:
Prati Dubey, Rakesh Kumar Mittan. Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review. International Journal of Optical Innovations & Research. 2025; 03(01):-.
How to cite this URL:
Prati Dubey, Rakesh Kumar Mittan. Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review. International Journal of Optical Innovations & Research. 2025; 03(01):-. Available from: https://journals.stmjournals.com/ijoir/article=2025/view=206771


References

  1. Gawande U, Hajari K, Golhar Y. Novel person detection and suspicious activity recognition using enhanced YOLOv5 and motion feature map. Artificial Intelligence Review. 2024 Jan 18;57(2):16.
  2. Tutar H, Güneş A, Zontul M, Aslan Z. A hybrid approach to improve the video anomaly detection performance of pixel-and frame-based techniques using machine learning algorithms. Computation. 2024 Jan 24;12(2):19.
  3. Selvakumar S. An effective framework of human abnormal behaviour recognition and tracking using multiscale dilated assisted residual attention network. Expert Systems with Applications. 2024 Aug 1;247:123264.
  4. Alruwais NM, Zakariah M. Student Recognition and Activity Monitoring in E-Classes Using Deep Learning in Higher Education. IEEE Access. 2024 Jan 17;12:66110-28.
  5. Wang J, Hu F, Abbas G, Albekairi M, Rashid N. Enhancing image categorization with the quantized object recognition model in surveillance systems. Expert Systems with Applications. 2024 Mar 15;238:122240.
  6. Daud A, Khan MB, Khattak AB, Tanoli SA, Mustafa A, Rehman M, López OL. Next-Generation Security: Detecting Suspicious Liquids Through Software Defined Radio Frequency Sensing and Machine Learning. IEEE Sensors Journal. 2024 Jan 15;24(5):7140-52.
  7. Talib M, Al-Noori AH, Suad J. YOLOv8-CAB: Improved YOLOv8 for Real-time object detection. Karbala International Journal of Modern Science. 2024;10(1):5.
  8. Sun ZW, Hua ZX, Li HC, Zhong HY. Flying bird object detection algorithm in surveillance video based on motion information. IEEE Transactions on Instrumentation and Measurement. 2023 Nov 28;73:1-5.
  9. Gautam D, Gupta H, Shekhar H, Kumar M, Nasser SJ, Fouad L. Suspicious Object Tracking With Yolov3 with Python Using Open-CV. In2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 2023 May 12 (pp. 1772-1775). IEEE.
  10. Ahmed A, Alansari M, Alnuaimi K, Velayudhan D, Hassan T, Werghi N. Detection transformer framework for recognition of heavily occluded suspicious objects. In2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2023 Jun 12 (pp. 1-6). IEEE.
  11. Pullakandam M, Loya K, Salota P, Yanamala RM, Javvaji PK. Weapon object detection using quantized yolov8. In2023 5th international conference on energy, power and environment: towards flexible green energy technologies (ICEPE) 2023 Jun 15 (pp. 1-5). IEEE.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 07/01/2025
Accepted 27/03/2025
Published 09/04/2025
Publication Time 92 Days
Total Visits: 48


My IP

PlumX Metrics