Cited by 58Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsVisual Attention and Saliency Detection

Object Detection and Tracking with UAV Data Using Deep Learning

Micheal, A. Ancy, Vani, K., Sanjeevi, S., Lin, Chao-Hung·2021·Journal of the Indian Society of Remote Sensing

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Crossref · 10.1007/s12524-020-01229-x

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20 of 20 references verified0 citation errors · 0 not checkable

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  • The YOLOv3 with Darknet-53 achieves an F1 score of 92.10% on a combined dataset of Custom, aerial-cars, and UAVDT.

    “[94] YOLOv3 | | Darknet-53 | Custom + aerial-cars dataset [73] + UAVDT | 2720 × 1530 (30fps) | Different altitudes | Different angles | -- | -- | 92.10 | -- | --”

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Authors
Micheal, A. A., Vani, K., Sanjeevi, S., & Lin, C.-H.
Affiliations
  • Anna University, Chennai (IN)
  • National Cheng Kung University (TW)
Corresponding author
Micheal, A. Ancy
Venue
Journal of the Indian Society of Remote Sensing
Published
2020-11-02
Volume
49
Issue
3
Pages
463-469
ISSN
  • 0255-660X (ISSN-L)
  • 0974-3006
OpenAlex
W3096582369
Cited by
58
References
20
Citations by year
2026: 5 · 2025: 19 · 2024: 7 · 2023: 17 · 2022: 9 · 2021: 1
FWCI
2.63
Type
journal article
Language
en
Open access
closed
Locations
Journal of the Indian Society of Remote Sensing (publishedVersion)
Funders
ISPRS Scientific Initiatives 2019
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
object detection, object tracking, UAV imagery, deep supervision, long short-term memory, domain mismatch

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