Notice of retraction
Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 6(1) (2020)
Copyright(C) MYU K.K.
pp. 1997-2006
S&M2235 Research Paper of Special Issue
Published: June 10, 2020

Application of Convolutional Neural Network (CNN)–AdaBoost Algorithm in Pedestrian Detection [PDF]

Guiyuan Li, Changfu Zong, Guangfeng Liu, and Tianjun Zhu

(Received December 26, 2019; Accepted April 8, 2020)

Keywords: pedestrian detection, autonomous driving, aggregate channel feature, convolutional neural networks

Pedestrian detection based on vision sensors is a hot and difficult issue in the field of autonomous driving. The large amount of data processing leads to high requirements for the robustness and real-time performance of the employed algorithm. The aggregate channel feature (ACF) algorithm is one of the widely recognized fast pedestrian detection algorithms, but there are many missed detections when the target is occluded or small. In response to this problem, we propose a pedestrian detection algorithm based on a combination of a five-layer convolutional neural network structure and an AdaBoost classifier (CNN–AdaBoost). The model was trained using Caltech and INRIA datasets, and detection experiments were performed using collected videos. The results show that the error detection rate of the proposed algorithm is greatly reduced compared with that of the ACF algorithm, but the detection speed is basically unchanged. Compared with the locally decorrelated channel features (LDCF) algorithm, the proposed algorithm achieves similar detection accuracy but the detection efficiency is greatly improved.

Corresponding author: Changfu Zong

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Cite this article
Guiyuan Li, Changfu Zong, Guangfeng Liu, and Tianjun Zhu, Application of Convolutional Neural Network (CNN)–AdaBoost Algorithm in Pedestrian Detection, Sens. Mater., Vol. 32, No. 6, 2020, p. 1997-2006.

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