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Single-Image Crowd Counting using Multi-Column Neural Network

by Rinku Mahesh Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 11
Year of Publication: 2020
Authors: Rinku Mahesh Sharma
10.5120/ijca2020920598

Rinku Mahesh Sharma . Single-Image Crowd Counting using Multi-Column Neural Network. International Journal of Computer Applications. 175, 11 ( Aug 2020), 31-35. DOI=10.5120/ijca2020920598

@article{ 10.5120/ijca2020920598,
author = { Rinku Mahesh Sharma },
title = { Single-Image Crowd Counting using Multi-Column Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 11 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number11/31499-2020920598/ },
doi = { 10.5120/ijca2020920598 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:46.868561+05:30
%A Rinku Mahesh Sharma
%T Single-Image Crowd Counting using Multi-Column Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 11
%P 31-35
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crowd scene understanding is an important and challenging problem in computer vision. Most of studies based on tracking individuals, crowd counting, finding region of motion and alarming crowd flaws have came into existence. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. One of the appropriate method that can accurately estimate the crowd count from an image with arbitrary crowd density and arbitrary perspective is using the state-of-the-art i.e. convolution neural network. The technique used for the crowd detection and crowd density estimation is through the Multicolumn Convolution Neural Network architecture. The model allows the input image to be of arbitrary size or resolution with high accuracy and produces a state of art results. The proposed work is implemented with the Shanghaitech dataset, which is among the largest dataset. The model produces highly precise and accurate results with the estimate crowd count and density map.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep learning Convolution neural network Crowd density Multicolumn Convolution Neural Network.

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