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Multi target tracking from drones by learning from generalized graph differences

Ardo, Hakan LU and Nilsson, Mikael LU (2020) 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 p.46-54
Abstract

Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. The weights of such network flow problem can be learnt efficiently from training data using a recently introduced concept called Generalized Graph Differences (GGD). This allows a general tracker implementation to be specialized to drone videos by training it on the VisDrone dataset. Two modifications to the original GGD is introduced in this paper and a result with an average precision of 23.09 on the test set of VisDrone 2019 was achieved.

Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Multi target tracking
host publication
Proceedings - 2019 International Conference on Computer Vision : Workshops, ICCVW 2019 - Workshops, ICCVW 2019
pages
9 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
conference location
Seoul, Korea, Republic of
conference dates
2019-10-27 - 2019-10-28
external identifiers
  • scopus:85082448559
ISBN
9781728150239
978-1-7281-5024-6
DOI
10.1109/ICCVW.2019.00012
language
English
LU publication?
yes
id
255202ed-4669-40ff-9748-32ea5fdedd69
date added to LUP
2020-05-27 13:40:05
date last changed
2024-08-21 21:58:40
@inproceedings{255202ed-4669-40ff-9748-32ea5fdedd69,
  abstract     = {{<p>Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. The weights of such network flow problem can be learnt efficiently from training data using a recently introduced concept called Generalized Graph Differences (GGD). This allows a general tracker implementation to be specialized to drone videos by training it on the VisDrone dataset. Two modifications to the original GGD is introduced in this paper and a result with an average precision of 23.09 on the test set of VisDrone 2019 was achieved.</p>}},
  author       = {{Ardo, Hakan and Nilsson, Mikael}},
  booktitle    = {{Proceedings - 2019 International Conference on Computer Vision : Workshops, ICCVW 2019}},
  isbn         = {{9781728150239}},
  keywords     = {{Multi target tracking}},
  language     = {{eng}},
  month        = {{03}},
  pages        = {{46--54}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Multi target tracking from drones by learning from generalized graph differences}},
  url          = {{http://dx.doi.org/10.1109/ICCVW.2019.00012}},
  doi          = {{10.1109/ICCVW.2019.00012}},
  year         = {{2020}},
}