Int. J. Simul. Multidisci. Des. Optim.
Volume 10, 2019
|Number of page(s)||9|
|Published online||23 September 2019|
Collaborative training of far infrared and visible models for human detection
Université Picardie Jules-Verne, 80000 Amiens, France
2 Université Technologique de Compiègne, 60200 Compiègne, France
* e-mail: firstname.lastname@example.org
Accepted: 27 August 2019
This paper is about the collaborative training of a far infrared and a visible spectrum human detector; the idea is to use the strengths of one detector to fill the weaknesses of the other detector and vice versa. At first infrared and visible human detectors are pre-trained using initial training datasets. Then, the detectors are used to collect as many detections as possible. The validity of each detection is tested using a low-level criteria based on an objectness measure. New training data are generated in a coupled way based on these detections and thus reinforce both the infrared and the visible human detectors in the same time. In this paper, we showed that this semi-supervised approach can significantly improve the performance of the detectors. This approach is a good solution to generate infrared training data, this kind of data being rarely available in the community.
© P. Blondel et al., published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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