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Detection of different classes moving object in public surveillance using artificial neural network (ANN)

Rashidan, M. Ariff and Mohd Mustafah, Yasir and Abdul Hamid, Syamsul Bahrin and Zainuddin, N. Afiqah and A. Aziz, Nor Nadirah (2014) Detection of different classes moving object in public surveillance using artificial neural network (ANN). In: International Conference on Computer and Communication Engineering (ICCCE 2014), 23rd – 25th September 2014, Sunway Putra Hotel, Kuala Lumpur.

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Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance Systems. Street crimes such as snatch theft is increasing drastically in recent years, cause a serious threat to human life worldwide. In this paper, a moving object detection and classification model was developed using novel Artificial Neural Network (ANN) simulation with the aim to identify its suitability for different classes of moving objects, particularly in public surveillance conditions. The result demonstrated that the proposed method consistently performs well with different classes of moving objects such as, motorcyclist, and pedestrian. Thus, it is reliable to detect different classes of moving object in public surveillance camera. It is also computationally fast and applicable for detecting moving objects in real-time.

Item Type: Conference or Workshop Item (Invited Papers)
Additional Information: 5107/41598
Uncontrolled Keywords: object detection; public surveillance; street crime; rate of occurrence; neural network
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr Yasir Mohd Mustafah
Date Deposited: 17 Feb 2015 09:36
Last Modified: 21 Sep 2017 17:33
URI: http://irep.iium.edu.my/id/eprint/41598

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