title: A high accuracy pedestrian detection system combining a cascade AdaBoost detector and random vector functional-link net.
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.
authors: Department of Electronics Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea.
authors: Zhihui
authors: 0000-0001-8140-1882
authors: Z
authors: Wang
authors: Department of Multimedia, Mokpo National University, Jeonnam 534-729, Republic of Korea.
authors: Sook
authors: S
authors: Yoon
authors: Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou 311121, China.
authors: Shan Juan
authors: SJ
authors: Xie
authors: Department of Electronics Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea.
authors: Yu
authors: 0000-0002-3685-6228
authors: Y
authors: Lu
authors: 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.
authors: Dong Sun
authors: DS
authors: Park
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans