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The Scientific World Journal
Volume 2014 (2014), Article ID 105089, 7 pages
http://dx.doi.org/10.1155/2014/105089
Research Article

A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

1Department of Electronics Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea
2Department of Multimedia, Mokpo National University, Jeonnam 534-729, Republic of Korea
3Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou 311121, China
4IT Convergence Research Center, Chonbuk National University, Jeonju 561-756, Republic of Korea

Received 27 March 2014; Revised 4 May 2014; Accepted 5 May 2014; Published 19 May 2014

Academic Editor: Yu-Bo Yuan

Copyright © 2014 Zhihui Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.