Collaborative training of far infrared and visible models for human detection - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue International Journal for Simulation and Multidisciplinary Design Optimization Année : 2019

Collaborative training of far infrared and visible models for human detection

Résumé

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.
Fichier principal
Vignette du fichier
smdo190012.pdf (2.05 Mo) Télécharger le fichier
Origine : Publication financée par une institution
Loading...

Dates et versions

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

Identifiants

Citer

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⟩
53 Consultations
40 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More