Citation

Wang Z, Yoon S, Xie SJ, Lu Y, Park DS. ScientificWorldJournal 2014; 2014: e105089.

Affiliation

Department of Electronics Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea ; IT Convergence Research Center, Chonbuk National University, Jeonju 561-756, Republic of Korea.

Copyright

(Copyright © 2014, ScientificWorld, Ltd.)

DOI

10.1155/2014/105089

PMID

24959598

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.

Language: en