Cyclist detection based on detection proposals and deep convolutional neural networks

LI Xiaofei; XU Qing; XIONG Hui; WANG Jianqiang; LI Keqiang

doi:10.16511/j.cnki.qhdxxb.2017.22.026

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Journal of Tsinghua University(Science and Technology) ›› 2017, Vol. 57 ›› Issue (5) : 491-496. DOI: 10.16511/j.cnki.qhdxxb.2017.22.026

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Cyclist detection based on detection proposals and deep convolutional neural networks

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Abstract

Cyclist protection systems based on cyclist detection methods are needed to protect cyclists from road traffic. This paper presents a detection proposal method and a cyclist detection method using deep convolutional neural networks to classify and locate cyclists. The detection proposal method uses cyclist shared salient region detection, redundancy-based detection and geometric constraint-based detection. Tests using a public cyclist dataset show that this method significantly outperforms state-of-the-art detection proposals, which verifies the effectiveness of this method.

Key words

object detection / cyclist detection / detection proposal / convolutional neural network

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LI Xiaofei, XU Qing, XIONG Hui, WANG Jianqiang, LI Keqiang. Cyclist detection based on detection proposals and deep convolutional neural networks[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(5): 491-496 https://doi.org/10.16511/j.cnki.qhdxxb.2017.22.026

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