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Presentation 2022-03-07 13:50
[Poster Presentation] Head location estimation using CSRNet for understanding Highly Congested Scenes
Takuya Nagatoshi, Michiharu Niimi (KIT) EMM2021-95
Abstract (in Japanese) (See Japanese page) 
(in English) Neural network for congested scene recognition called CSRNet has been proposed. The purpose of CSRNet is to count the number of heads based on the output of CSRNet. We plane to apply CSRNet to the attendance management system which is able to recognize who are attending in a classroom, in order to do that, we need to estimate head location. In CSRNet, the input is an RGB image that is taken for a scene, and the output is the density map of people. The number of heads is given by the integral of density map. The learning process of CSRNet is performed by decreasing the mean square error between the output and the correct density map. Note that the size of density map is 1/8 of input image. Because the density map can be regarded as 2-dimentinal signal, we can estimate head locations by determining its maximal value. In this report, we put an up-sampling layer inside of CSRNet to try to make a detailed density map. The size of density map become 1/4 of input image. We apply gaussian filters to density map to reduce the influence of noise. In the experiments, we used ShanghaiTech data set and real pictures with is taken at a real classroom. As a result of experiments, we confirmed that the effectiveness of adding up-sampling layer, but it is difficult to adjust head location error and multiple extraction.
Keyword (in Japanese) (See Japanese page) 
(in English) congested scene recognition / head location estimation / CSRNet / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 417, EMM2021-95, pp. 17-22, March 2022.
Paper # EMM2021-95 
Date of Issue 2022-02-28 (EMM) 
ISSN Online edition: ISSN 2432-6380
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Conference Information
Committee EMM  
Conference Date 2022-03-07 - 2022-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) (Primary: Online, Secondary: On-site) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. 
Paper Information
Registration To EMM 
Conference Code 2022-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Head location estimation using CSRNet for understanding Highly Congested Scenes 
Sub Title (in English)  
Keyword(1) congested scene recognition  
Keyword(2) head location estimation  
Keyword(3) CSRNet  
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1st Author's Name Takuya Nagatoshi  
1st Author's Affiliation Kyushu Institute of Technology (KIT)
2nd Author's Name Michiharu Niimi  
2nd Author's Affiliation Kyushu Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2022-03-07 13:50:00 
Presentation Time 15 minutes 
Registration for EMM 
Paper # EMM2021-95 
Volume (vol) vol.121 
Number (no) no.417 
Page pp.17-22 
#Pages
Date of Issue 2022-02-28 (EMM) 


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