Computer vision based intruder detection framework (CV-IDF)

Surveillance has become a major issue in recent years after food and clothing, the focus is how to prevent sudden attacks to secure our lives. In this paper, we propose a robust algorithm which aims at detecting and tracking multiple intruders in a forbidden area. Significant additions of our paper...

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Bibliographic Details
Published in:	2017 2nd International Conference on Computer and Communication Systems (ICCCS) pp. 41 - 45
Main Authors:	Shahid, Aasma, Tayyab, Alina, Mehmood, Musfira, Anum, Rida, Jalil, Abdul, Ali, Ahmad, Ali, Haider, Ahmed, Javed
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2017
Subjects:	Classification algorithms Gaussian mixture model hungarian algorithm intruder detection Kalman filter Kalman filters object detection object tracking Prediction algorithms Target tracking
Online Access:	Get full text
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Summary:	Surveillance has become a major issue in recent years after food and clothing, the focus is how to prevent sudden attacks to secure our lives. In this paper, we propose a robust algorithm which aims at detecting and tracking multiple intruders in a forbidden area. Significant additions of our paper are that we propose a reliable and efficient framework which uses background subtraction method using Gaussian mixture models followed by Kalman filter to detect intruders in a restricted area, which reduces false alarms. It tracks multiple intruders moving inside a restricted region using Kalman filter, data associations of predictions obtained by Kalman filter and track of individual intruder is achieved by Hungarian algorithm. Alarms are generated after getting detections to notify security staff for taking appropriate actions.
ISBN:	1538605384 9781538605387
DOI:	10.1109/CCOMS.2017.8075263