Analysis of artificial neural network and viola-jones algorithm based moving object detection

Rashidan, M. Ariff and Mohd Mustafah, Yasir and Zainal Abidin, Zulkifli and Zainuddin, N. Afiqah and A. Aziz, Nor Nadirah (2014) Analysis of artificial neural network and viola-jones algorithm based moving object detection. In: International Conference on Computer and Communication Engineering (ICCCE 2014), 23rd – 25th September 2014, Sunway Putra Hotel, Kuala Lumpur.

	PDF Restricted to Repository staff only Download (1MB) | Request a copy
Preview	PDF Download (447kB) | Preview

Abstract

In recent years, the worrying rate of street crime has demanded more reliable and efficient public surveillance system. Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. Both methods are compared based on their precision of correctly classify the moving objects. The emphasis is on two major issues involve in the analysis of moving object detection, and object classification to two groups, pedestrian and motorcycle. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using two types of dataset, which are different in term of complexity of the background. The utilization of cascade architecture to the extracted features, benefits the algorithm. The algorithms have been tested on simulated events, and the more suitable algorithm with high detection rate is expected to be presented in this paper.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	5107/41602
Uncontrolled Keywords:	moving object detection; object classification; public surveillance
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
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 10:40
Last Modified:	19 Sep 2017 20:03
URI:	http://irep.iium.edu.my/id/eprint/41602

Actions (login required)

View Item