Intelligent Surveillance System Suspicious Activity Tracking With Yolov8 and Vision Transformer

DOI

10.2991/978-94-6463-858-5_24How to use a DOI?

Keywords

Intelligence Surveillance System; YOLO v8; Vision Transformer; Suspicious Activity Detection; Machine Learning

Abstract

The paper introduces a novel approach as security threats continue to evolve in complexity and dynamism, the demand for advanced real-time surveillance systems has become increasingly critical. This paper presents a novel approach by integrating two state-of-the-art technologiesYOLOv8 (You Only Look Once version 8) and Vision Transformer (ViT) to develop an efficient and accurate intelligent surveillance framework. YOLOv8 is renowned for its high-speed and precise object detection capabilities across diverse environments, whereas Vision Transformers leverage attention mechanisms to enhance contextual understanding and visual data classification. The study critically reviews existing literature to assess the performance and application domains of these technologies in suspicious activity detection. By examining their individual strengths, limitations, and potential synergies, this research proposes an integrated model that combines their capabilities to improve surveillance effectiveness. The objective is to provide a comprehensive guide for researchers and practitioners in developing robust surveillance solutions that enhance both public and private security infrastructures. Furthermore, the study emphasizes ethical considerations to ensure responsible deployment of surveillance technologies while contributing to the creation of safer environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Md Karaamathullah Sheriff
AU  - M. Mohammed Thah
PY  - 2025
DA  - 2025/11/04
TI  - Intelligent Surveillance System Suspicious Activity Tracking With Yolov8 and Vision Transformer
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 264
EP  - 275
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_24
DO  - 10.2991/978-94-6463-858-5_24
ID  - Sheriff2025
ER  -