Crowd Counting Via Residual Building Block Convolutional Neural Network

被引:0
作者
Xue, Yaokai [1 ]
Li, Jing [1 ]
机构
[1] Nanchang Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China
来源
2019 3RD INTERNATIONAL SYMPOSIUM ON AUTONOMOUS SYSTEMS (ISAS 2019) | 2019年
基金
中国国家自然科学基金;
关键词
crowd counting; residual; building block; convolutional neural network; density map and VGG;
D O I
10.1109/isass.2019.8757730
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We present a new method called residual building block convolutional neural network (RBB-CNN) for generating high-quality density maps and count estimation by applying stacked residual building blocks. The specific deploy of convolution layers in building blocks are inspired by the work of VGG16. The RBB-CNN is an easy-trained end-to-end model and allows arbitrary-size input because of its pure convolutional structure. To verify the validation of the residual building block, an ablation on ShanghaiTech Part A is implemented. Meanwhile, we demonstrate the performance of RBB-CNN on three crowd counting datasets, i.e., ShanghaiTech, UCSD and MALL. With a wide range from dense to sparse density, our model achieves the state-of-the-art performance on all of the above datasets.
引用
收藏
页码:187 / 192
页数:6
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