Deep Classification Technique for Density Counting

被引：0

作者：

Al-hadhrami, Suheer ^{[1
]}

Altuwaijri, Sarah ^{[1
]}

Alkharashi, Norah ^{[1
]}

Ouni, Ridha ^{[1
]}

机构：

[1] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

来源：

2019 2ND INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS) | 2019年

关键词：

Density counting; deep learning; CNN-convolution neural network; classification; switch-CNN; single CNN; multi-column CNN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Crowd counting, resulted from extensive analysis, is reflected by many aspects such as appearance similarity between people, background components and the inter-blocking in intense crowds. Current research is challenging these aspects by applying different types of architectures. In this paper, we propose a single conventional neural network for density counting based on four conventional layers. A comparison of our proposed network with Switched Conventional Neural Networks (Switch-CNN) approaches has been performed in order to evaluate its performance in terms of accuracy and loss. As a result, several experiments prove the effectiveness and efficiency of the proposed method. We got 94.6% and 0.2625 for both accuracy and loss respectively.

引用

页数：6

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