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In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tra...

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Bibliographic Details
Published in전기학회 논문지 P권, 67(4) Vol. 67P; no. 4; pp. 227 - 232
Main Author 서창진(Chang Jin Seo)
Format Journal Article
LanguageKorean
Published 대한전기학회 2018
Subjects
전기공학
Deep learning
Target tracking
YOLOv3
UAV
Kalman filter
Object detection
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ISSN1229-800X
2586-7792

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Summary:In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tracking algorithms. So we applied to the YOLOv3 algorithm which is the best detection algorithm now at our proposed detecting system and also used the Kalman Filter algorithm that uses a variable detection area as the tracking system. In the experiment result, we could find the proposed system is an excellent result more than a fixed area detection system.
Bibliography:KISTI1.1003/JNL.JAKO201809454743034
ISSN:1229-800X
2586-7792