ML Anthology

Multi Target Tracking from Drones by Learning from Generalized Graph Differences

Håkan Ardö, Mikael G. Nilsson
ICCVW 2019 pp. 46-54
doi:10.1109/ICCVW.2019.00012 /iccvw/2019/ardo2019iccvw-multi/

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.

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Cite

Text

Ardö and Nilsson. "Multi Target Tracking from Drones by Learning from Generalized Graph Differences." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00012

Markdown

[Ardö and Nilsson. "Multi Target Tracking from Drones by Learning from Generalized Graph Differences." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/ardo2019iccvw-multi/) doi:10.1109/ICCVW.2019.00012

BibTeX

@inproceedings{ardo2019iccvw-multi,
  title     = {{Multi Target Tracking from Drones by Learning from Generalized Graph Differences}},
  author    = {Ardö, Håkan and Nilsson, Mikael G.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {46-54},
  doi       = {10.1109/ICCVW.2019.00012},
  url       = {https://mlanthology.org/iccvw/2019/ardo2019iccvw-multi/}
}