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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