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Journal Articles International Journal for Simulation and Multidisciplinary Design Optimization Year : 2019

Collaborative training of far infrared and visible models for human detection

Abstract

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.
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Dates and versions

hal-02407464 , version 1 (16-10-2020)

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Paul Blondel, Alex Potelle, Claude Pégard, Rogelio Lozano. Collaborative training of far infrared and visible models for human detection. International Journal for Simulation and Multidisciplinary Design Optimization, 2019, 10, pp.A15. ⟨10.1051/smdo/2019016⟩. ⟨hal-02407464⟩
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