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JACIII Vol.21 No.5 pp. 834-839
doi: 10.20965/jaciii.2017.p0834
(2017)

Paper:

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Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network

Qin Qin*,† and Josef Vychodil**

*School of Computer, Henan University of Engineering
Henan, Zhengzhou 451191, China

**Department of Radio Electronics, Brno University of Technology
Technicka 3082/12,616 00 Brno, Czech Republic

†Corresponding author

Received:
February 9, 2017
Accepted:
July 28, 2017
Published:
September 20, 2017
Creative Commons License
Keywords:
convolutional neural network, multi-feature fusion, pedestrian detection, supervised learning network, image recognition
Abstract

This paper proposes a new multi-feature detection method of local pedestrian based on a convolutional neural network (CNN), which provides a reliable basis for multi-feature fusion in pedestrian detection. According to the standard of pedestrian detection ratio, the pedestrian under the detection window would be segmented, using the sample labels to guide the local characteristics of CNN learning, the supervised learning after the network can obtain the local feature fusion more pedestrian description ability. Finally, a large number of experiments have been performed. The experimental results show that the local features of the neural network are better than those of most pedestrian features and combination features.

Cite this article as:
Q. Qin and J. Vychodil, “Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.5, pp. 834-839, 2017.
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