Intelligent Room Surveillance: AI-Based Object Tracking and Missing Item Detection

DOI

10.2991/978-94-6239-654-8_47How to use a DOI?

Keywords

AI-Based Surveillance; Object Tracking; Missing Item Detection; Computer Vision

Abstract

In an age of artificial intelligence and automation, intelligent surveillance systems have become essential in asset management and safety. The system proposed here, a smart surveillance system, utilizes AI-enabled computer vision algorithms that can monitor objects and recognize the absence of an object or objects, or determine it (or them) were removed or misplaced in real time. The system continuously monitors the environment of a room, recognizes objects, tracks their movement, and notifies alert users if an object is displaced or removed. The system uses deep learning models of detection, classification, and tracking of objects of interest with advanced algorithms, including YOLO and DeepSORT, to accurately track and analyze objects’ movement. When an object is detected to be unfound, the system identifies that object’s last known location and alerts users either via an intuitive dashboard or throughout a push notification. The system can integrate with IoT devices, including but not limited to smart cameras, smart sensors, and forms of smart technology to make the system accurate and efficient. The purpose of the system was to be able to monitor for security across various settings such as home, office space, warehouse, or any other surveillance setting. The proposed system can be implemented in a scalable and cost-effective way to provide enhanced monitoring capabilities and to reduce the amount of manual monitoring in the same day-to-day observational settings as manual observations. Using AI, IoT, and real-time analytics, the proposed system develops a proactive and predictive outreach of modern surveillance to improve security, reduce losses from assets and improve monitoring protocol efficiency.

Copyright

© 2026 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  - N. Meena Kumari Bugatha
AU  - Nasrrin Kalifathulla Khan
PY  - 2026
DA  - 2026/04/24
TI  - Intelligent Room Surveillance: AI-Based Object Tracking and Missing Item Detection
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 585
EP  - 595
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_47
DO  - 10.2991/978-94-6239-654-8_47
ID  - Bugatha2026
ER  -