Real-time low-pixel infrared human detection from unmanned aerial vehicles

Ignacio Martinez-Alpiste; Gelayol Golcarenarenji; Qi Wang; Jose Maria Alcaraz-Calero

doi:10.1145/3416014.3424600

Real-time low-pixel infrared human detection from unmanned aerial vehicles

Ignacio Martinez-Alpiste
, Gelayol Golcarenarenji
, Qi Wang
, Jose Maria Alcaraz-Calero

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

204 Downloads (Pure)

Abstract

To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.

Original language	English
Title of host publication	DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications
Publisher	Association for Computing Machinery
Pages	9-15
Number of pages	7
ISBN (Electronic)	9781450381215
DOIs	https://doi.org/10.1145/3416014.3424600
Publication status	Published - 16 Nov 2020
Event	10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020 - Virtual, Online, Spain Duration: 16 Nov 2020 → 20 Nov 2020

Publication series

Name	DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Conference

Conference	10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020
Country/Territory	Spain
City	Virtual, Online
Period	16/11/20 → 20/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 10 Reduced Inequalities

Keywords

Jetson AGX Xavier
machine learning
thermal imagery
UAV
YOLO

Access to Document

10.1145/3416014.3424600

2020 09 15 Martinez-Alpiste et al Real-time acceptedAccepted author manuscript, 1.59 MB

Cite this

Martinez-Alpiste, I., Golcarenarenji, G., Wang, Q., & Alcaraz-Calero, J. M. (2020). Real-time low-pixel infrared human detection from unmanned aerial vehicles. In DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (pp. 9-15). Article 3424600 (DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications). Association for Computing Machinery. https://doi.org/10.1145/3416014.3424600

Martinez-Alpiste, Ignacio ; Golcarenarenji, Gelayol ; Wang, Qi et al. / Real-time low-pixel infrared human detection from unmanned aerial vehicles. DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery, 2020. pp. 9-15 (DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications).

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title = "Real-time low-pixel infrared human detection from unmanned aerial vehicles",

abstract = "To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26\% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition. ",

keywords = "Jetson AGX Xavier, machine learning, thermal imagery, UAV, YOLO",

author = "Ignacio Martinez-Alpiste and Gelayol Golcarenarenji and Qi Wang and Alcaraz-Calero, \{Jose Maria\}",

note = "Funding Information: This work was in part funded by the Innovation Centre for Sensor and Imaging Systems (CENSIS) and Thales UK, and in collaboration with Police Scotland, under the grant {"}Smart Unmanned Aerial System for Real-Time Object Detection{"} (Ref. CAF440). The authors would like to thank all the partners in this project for their support Publisher Copyright: {\textcopyright} 2020 ACM. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020 ; Conference date: 16-11-2020 Through 20-11-2020",

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language = "English",

series = "DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications",

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Martinez-Alpiste, I, Golcarenarenji, G, Wang, Q & Alcaraz-Calero, JM 2020, Real-time low-pixel infrared human detection from unmanned aerial vehicles. in DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications., 3424600, DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, Association for Computing Machinery, pp. 9-15, 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020, Virtual, Online, Spain, 16/11/20. https://doi.org/10.1145/3416014.3424600

Real-time low-pixel infrared human detection from unmanned aerial vehicles. / Martinez-Alpiste, Ignacio; Golcarenarenji, Gelayol; Wang, Qi et al.
DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery, 2020. p. 9-15 3424600 (DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Real-time low-pixel infrared human detection from unmanned aerial vehicles

AU - Martinez-Alpiste, Ignacio

AU - Golcarenarenji, Gelayol

AU - Wang, Qi

AU - Alcaraz-Calero, Jose Maria

N1 - Funding Information: This work was in part funded by the Innovation Centre for Sensor and Imaging Systems (CENSIS) and Thales UK, and in collaboration with Police Scotland, under the grant "Smart Unmanned Aerial System for Real-Time Object Detection" (Ref. CAF440). The authors would like to thank all the partners in this project for their support Publisher Copyright: © 2020 ACM. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/11/16

Y1 - 2020/11/16

N2 - To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.

AB - To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.

KW - Jetson AGX Xavier

KW - machine learning

KW - thermal imagery

KW - UAV

KW - YOLO

UR - https://www.scopus.com/pages/publications/85096978925

U2 - 10.1145/3416014.3424600

DO - 10.1145/3416014.3424600

M3 - Conference contribution

AN - SCOPUS:85096978925

T3 - DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

SP - 9

EP - 15

BT - DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

PB - Association for Computing Machinery

T2 - 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2020

Y2 - 16 November 2020 through 20 November 2020

ER -

Martinez-Alpiste I, Golcarenarenji G, Wang Q, Alcaraz-Calero JM. Real-time low-pixel infrared human detection from unmanned aerial vehicles. In DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications. Association for Computing Machinery. 2020. p. 9-15. 3424600. (DIVANet 2020 - Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications). doi: 10.1145/3416014.3424600

Real-time low-pixel infrared human detection from unmanned aerial vehicles

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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