Cyclist detection based on detection proposals and deep convolutional neural networks
LI Xiaofei1, XU Qing1, XIONG Hui1,2, WANG Jianqiang1, LI Keqiang1
1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
2. School of Software, Beihang University, Beijing 100191, China
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.
李晓飞, 许庆, 熊辉, 王建强, 李克强. 基于候选区域选择及深度网络模型的骑车人识别[J]. 清华大学学报（自然科学版）, 2017, 57(5): 491-496.
LI Xiaofei, XU Qing, XIONG Hui, WANG Jianqiang, LI Keqiang. Cyclist detection based on detection proposals and deep convolutional neural networks. Journal of Tsinghua University(Science and Technology), 2017, 57(5): 491-496.
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2017, Vol. 57 
