Multi Target Tracking from Drones by Learning from Generalized Graph Differences

Hakan Ardo, Mikael Nilsson; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

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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[bibtex]
@InProceedings{Ardo_2019_ICCV,
author = {Ardo, Hakan and Nilsson, Mikael},
title = {Multi Target Tracking from Drones by Learning from Generalized Graph Differences},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}