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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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Contributor : Rogelio Lozano <>
Submitted on : Friday, October 16, 2020 - 5:55:16 AM
Last modification on : Wednesday, October 21, 2020 - 3:02:28 AM


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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, EDP sciences/NPU (China), 2019, 10, pp.A15. ⟨10.1051/smdo/2019016⟩. ⟨hal-02407464⟩



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