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CCTV Intelligent Surveillance on Intruder Detection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Kajenthani Kanthaseelan, Paskaran Pirashaanthan, Jasmin Jelaxshana A.A.P, Akshaya Sivaramakrishnan, Kavinga Yapa Abeywardena, Tharika Munasinghe

Kajenthani Kanthaseelan, Paskaran Pirashaanthan, Jasmin Jelaxshana A.A.P, Akshaya Sivaramakrishnan, Kavinga Yapa Abeywardena and Tharika Munasinghe. CCTV Intelligent Surveillance on Intruder Detection. International Journal of Computer Applications 174(14):29-34, January 2021. BibTeX

	author = {Kajenthani Kanthaseelan and Paskaran Pirashaanthan and Jasmin Jelaxshana A.A.P and Akshaya Sivaramakrishnan and Kavinga Yapa Abeywardena and Tharika Munasinghe},
	title = {CCTV Intelligent Surveillance on Intruder Detection},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2021},
	volume = {174},
	number = {14},
	month = {Jan},
	year = {2021},
	issn = {0975-8887},
	pages = {29-34},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2021921035},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the technology today, the detection of intruders was introduced to decrease theft and crimes. The implementation of this system is made at home. A system with just records of theft would not work, the system should be efficient and faster to detect thefts effortlessly. The goal of this system is to give a new direction of innovation in the closed- circuit television video image processing in computer vision. Having a system to monitor and notify users of potential threats must be implemented, as well as predicting a child’s movements at home. Doing this in real-time ensures a lot of human labor to be minimized while also ensuring recognition using computer vision. This document provides an outline of the intrusion detection system and danger prevention system for children outside and inside the home environment, with the help of image processing, face recognition, and training of the model through machine learning. For an instance, if a kid at home reached near the well in the garden area, system predicts whether the kid is reaching the well by deriving it from the distance calculated earlier and sends an alert to the mobile or any device where an application is installed. Same prediction applies for any intruders to reach the home or any restricted places at home.


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Intrusion, image processing, face detection, rule-based notification