Le mercredi 16 Décembre, à 14h, Mr Yannick Zoetgnande soutient sa thèse intitulée: "Fall detection and activity recognition using stereo low-resolution thermal imaging", devant le jury composé de:
- Christine Fernandez-Maloigne, Université de Poitiers (reviewer)
- Mohamed Daoudi, Université de Lille (reviewer)
- Antoine Manzanera, ENSTA ParisTech (examiner)
- Mireille Garreau, Université de Rennes 1 (examiner)
- Vincent Gauthier, Neotec Vision (examiner)
- Jean-Louis Dillenseger, University of Rennes 1 (director)
Nowadays, it is essential to find solutions to detect and prevent the falls of seniors. We proposed a low-cost device based on a pair of thermal sensors. The counterpart of these low-cost sensors is their low resolution (80x60 pixels), low refresh rate, noise, and halo effects. We proposed some approaches to bypass these drawbacks. First, we proposed a calibration method with a grid adapted to the thermal image and a framework ensuring the robustness of the parameters estimation despite the low resolution. Then, for 3D vision, we proposed a threefold sub-pixel stereo matching framework (called ST for Subpixel Thermal): 1) robust features extraction method based on phase congruency, 2) matching of these features in pixel precision, and 3) refined matching in sub-pixel accuracy based on local phase correlation. We also proposed a super-resolution method called Edge Focused Thermal Super-resolution (EFTS), which includes an edge extraction module enforcing the neural networks to focus on the edge in images. After that, for fall detection, we proposed a new method (called TSFD for Thermal Stereo Fall Detection) based on stereo point matching but without calibration and the classification of matches as on the ground or not on the ground. Finally, we explored many approaches to learn activities from a limited amount of data for seniors activity monitoring.