March 2017 –
June 2021
Toulouse, FR
PhD Student
LAAS / ACTIA Automotive
Based on our previous knowledge of deep learning techniques in passenger counting applications, we answer the industrial constraints of passenger counting in city buses through a deep architecture able to deal with images taken from low cost 2D sensors placed above the doorstep, from a zenithal point of view.
The scientific breakthrough related to deep learning applied to computer vision as well as the system embedding requirements motivate us to propose a unified and lightweight convolutional architecture in the context of multi-object tracking-by-detection for trajectory reconstruction.