Web-Based Intelligence Surveillance System to Detect Area Intrusion Using RT-DETR with Limited Dataset

Abstract:

Property intrusion remains a global concern, often driven by motives such as theft. While human-monitored CCTV systems are commonly employed for prevention, they are inherently limited by human capabilities and challenged by factors such as the increasing number of areas requiring surveillance and other factors. Advancements in Machine Learning (ML) offer promising solutions to overcome the limitations of human-monitored CCTV systems. Object detection, a potent ML method, enables real-time identification and tracking of objects through CCTV cameras. RT-DETR is an example of the excellent object detection model in terms of both accuracy and inference speed. The intention of this research is to implement RT-DETR on a web-based application to prevent property intrusion with such limited dataset quantity, for live video stream using NVIDIA RTX 3050 Mobile Laptop, which is a common and often considered as a mid-range GPU hardware. In addition to customizable surveillance zone definition, the application incorporates features to minimize false alarms, ensuring a more reliable and efficient security solution. Additionally, the application is equipped with automatic video recording as evidence of intrusion evidence.

• Periodical: Engineering Headway (Volume 27)
• Pages: 13-22
• DOI: https://doi.org/10.4028/p-rzSna2
• Online since: October 2025
• Authors: Kevin Agusto Sastramiharja*, Ganjar Alfian
• Keywords: Intelligent Surveillance System (ISS), Machine Learning, Object Detection, RT-DETR, Web-Application

References

[1] S.A. Velastin, "CCTV Video Analytics: Recent Advances and Limitations," in Visual Informatics: Bridging Research and Practice, vol. 5857, H. Badioze Zaman, P. Robinson, M. Petrou, P. Olivier, H. Schröder, and T. K. Shih, Eds., in Lecture Notes in Computer Science, vol. 5857. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, p.22–34.

DOI: 10.1007/978-3-642-05036-7_3

[2] B. D. Kaur, "Detection of human intrusion in a smart home".

[3] X. Wu, D. Sahoo, and S.C.H. Hoi, "Recent advances in deep learning for object detection," Neurocomputing, vol. 396, p.39–64, Jul. 2020.

DOI: 10.1016/j.neucom.2020.01.085

[4] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," 2015, arXiv.

DOI: 10.1109/tpami.2016.2577031

[5] R. Nayak, M.M. Behera, U.C. Pati, and S.K. Das, "Video-based Real-time Intrusion Detection System using Deep-Learning for Smart City Applications," in 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), GOA, India: IEEE, Dec. 2019, p.1–6.

DOI: 10.1109/ANTS47819.2019.9117960

[6] Y. Zhao et al., "DETRs Beat YOLOs on Real-time Object Detection," 2023, arXiv.

[7] R. Girshick, "Fast R-CNN," 2015, arXiv.

[8] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-End Object Detection with Transformers," 2020, arXiv.

DOI: 10.1007/978-3-030-58452-8_13

[9] A.Vaswani et al., "Attention Is All You Need," 2017, arXiv.

[10] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature Pyramid Networks for Object Detection," 2016, arXiv.

DOI: 10.1109/cvpr.2017.106

[11] Z. Yao, J. Ai, B. Li, and C. Zhang, "Efficient DETR: Improving End-to-End Object Detector with Dense Prior," 2021, arXiv.

[12] T.-Y. Lin et al., "Microsoft COCO: Common Objects in Context," 2014, arXiv.

[13] A. Ehsan, M. A. M. E. Abuhaliqa, C. Catal, and D. Mishra, "RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions," Appl. Sci., vol. 12, no. 9, p.4369, Apr. 2022.

DOI: 10.3390/app12094369

[14] C.-Y. Wang, I.-H. Yeh, and H.-Y. M. Liao, "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information," 2024, arXiv.

[15] V. Pimentel and B. G. Nickerson, "Communicating and Displaying Real-Time Data with WebSocket," IEEE Internet Comput., vol. 16, no. 4, p.45–53, Jul. 2012.

DOI: 10.1109/MIC.2012.64