DocumentCode
1738886
Title
A neural network based intelligent intruders detection and tracking system using CCTV images
Author
Fung, Chun Che ; Jerrat, Nicholas
Author_Institution
Sch. of Electr. & Comput. Eng., Curtin Univ. of Technol., Bentley, WA, Australia
Volume
2
fYear
2000
fDate
2000
Firstpage
409
Abstract
This paper reports the development of a neural network based intelligent intruders detection and tracking system using closed-circuit television (CCTV) images. It examines the techniques and algorithms used to identify a potential intruder and methods to eliminate other non-threatening objects. Once the presence of an intruder is determined, the object will be monitored and tracked. The tracked information can be used to further identify any suspicious behaviour in the sparse and complex environments. The traditional approach to intelligent scene monitoring (ISM) is examined and compared with the artificial neural network (ANN) approach. The ANN approach demonstrates how a system can learn how to distinguish suspicious movements from non-suspicious movements. The proposal has a potential to be used as an intelligent surveillance system
Keywords
closed circuit television; computerised monitoring; image motion analysis; learning (artificial intelligence); perceptrons; surveillance; tracking; video signal processing; ANN; CCTV images; artificial neural network; closed-circuit television; intelligent intruders detection system; intelligent intruders tracking system; intelligent scene monitoring; intelligent surveillance system; neural network training; nonsuspicious movements; object monitoring; object tracking; straight-through perceptron neural network; suspicious movements; video motion detection; Artificial intelligence; Artificial neural networks; Intelligent networks; Intelligent systems; Layout; Monitoring; Neural networks; Proposals; Surveillance; TV;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON 2000. Proceedings
Conference_Location
Kuala Lumpur
Print_ISBN
0-7803-6355-8
Type
conf
DOI
10.1109/TENCON.2000.888772
Filename
888772
Link To Document