Cyclist detection based on detection proposals and deep convolutional neural networks

doi:10.16511/j.cnki.qhdxxb.2017.22.026

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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.

Keywords object detection cyclist detection detection proposal convolutional neural network

ZTFLH:

TP391.4

Issue Date: 15 May 2017

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	LI Xiaofei
	XU Qing
	XIONG Hui
	WANG Jianqiang
	LI Keqiang

Cite this article:

LI Xiaofei,XU Qing,XIONG Hui, et al. Cyclist detection based on detection proposals and deep convolutional neural networks[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 491-496.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.22.026 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I5/491

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