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 - http://www.scopus.com/inward/record.url?scp=85096978925&partnerID=8YFLogxK
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 -
