Computer vision based intruder detection framework (CV-IDF)
Aasma Shahid, Alina Tayyab, M. Adil Mehmood, Rida Anum, Abdul Jalil, Ahmad Ali, Haider Ali, Javed Ahmed · 2017
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.