Applying Faster R-CNN In Extremely Low-Resolution Thermal Images for People Detection

被引：4

作者：

Jimenez-Bravo, Diego M. ^{[1
]}

Mutombo, Pierre Masala ^{[2
]}

Braem, Bart ^{[2
]}

Marquez-Barja, Johann M. ^{[2
]}

机构：

[1] Univ Salamanca, Fac Sci, Expert Syst & Applicat Lab, Plaza Caidos S-N, Salamanca 37002, Spain

[2] Univ Antwerp, IDLab Res Grp, IMEC, Sint Pietersvliet 7, B-2020 Antwerp, Belgium

来源：

PROCEEDINGS OF THE 2020 IEEE/ACM 24TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED SIMULATION AND REAL TIME APPLICATIONS (DS-RT) | 2020年

基金：

欧盟地平线“2020”;

关键词：

Convolutional Neural Network; Faster Region-Convolutional Neural Network; Grid Eye; Internet of Things; Low-Resolution Images; People Detection; Thermal Images;

D O I：

10.1109/ds-rt50469.2020.9213609

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In today's cities, it is increasingly normal to see different systems based on Artificial Intelligence (AI) that help citizens and government institutions in their daily lives. This is possible thanks to the Internet of Things (IoT). In this paper we present a solution using low-resolution thermal sensors in combination of deep learning to detect people in the images generated by those sensors. To verify whether the deep learning techniques are appropriate for this type of images of such low resolution, we have implement a Faster Region-Convolutional Neural Network. The results obtained are hopeful and undoubtedly encourage to continue improving this research line. With a perception of 72.85% and given the complexity of the problem presented we consider the results obtained to be highly satisfactory and it encourages us to continue improving the work presented in this article.

引用

页码：37 / 40

页数：4

共 15 条

[1]

Bardool Kevin, KBARDOOL KERAS FRCNN

[2]

Beltran Alex., 2013, Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, P1

[3]

Colby Sandra L., 2015, TECH REP

[4]

Erickson VarickL., 2013, Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, P1

[5] People counting based on head detection combining Adaboost and CNN in crowded surveillance environment [J].

Gao, Chenqiang ;

Li, Pei ;

Zhang, Yajun ;

Liu, Jiang ;

Wang, Lan .

NEUROCOMPUTING, 2016, 208 :108-116

[6]

Ha KN, 2006, 2006 SICE-ICASE INTERNATIONAL JOINT CONFERENCE, VOLS 1-13, P5074

[7]

Jeong Y, 2014, 2014 IEEE SENSORS APPLICATIONS SYMPOSIUM (SAS), P97, DOI 10.1109/SAS.2014.6798925

[8] Multi-Human Locating in Real Environment by Thermal Sensor [J].

Kuki, Masato ;

Nakajima, Hiroshi ;

Tsuchiya, Naoki ;

Hata, Yutaka .

2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, :4623-4628

[9] A Real-time Effective System for Tracking Passing People Using a Single Camera [J].

Ma, Honglian ;

Lu, Huchuan ;

Zhang, Mingxiu .

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, :6173-6177

[10] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J].

Ren, Shaoqing ;

He, Kaiming ;

Girshick, Ross ;

Sun, Jian .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (06) :1137-1149

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