Research team: RAP, Robotic department -- LAAS-CNRS, Toulouse, France
Advisors: Maxime Escourrou (maxime.escourrou@laas.fr), Ariane Herbulot (ariane.herbulot@laas.fr)
Duration: 4 -- 6 months
Schooling level: Master
Starting date: Flexible around March 2025

Keywords: Perception, Computer Vision, Data fusion, Robustness

The aim of this internship is to evaluate the results of several object tracking methods based on computer vision (camera), in order to identify and quantify the limitations of each method. A second step will be to propose a combination of multiple methods to get the best of each. Experimental evaluations will be carried out on public datasets and with Intel RealSense 3D cameras.

Profile:
[Expected] The candidate should hold a bachelor's degree in robotics, engineering, computer science (or related fields), and must pursue a master's degree in the same fields.
[Expected] Good programming skills (Python and/or C++).
[Expected] Good knowledge of machine learning and computer vision techniques.
[Expected] Interest in the field of robotics or computer vision.
[Appreciated] Experience with machine learning libraries such as PyTorch or Tensorflow.
[Appreciated] Knowledge of robotics and data fusion would be a plus.

More details on: https://www.laas.fr/fr/offres/masters-internship-evaluation-and-combination-of-perception-methods-in-robotics/

Application:

Candidates wishing to apply should send their CV and a cover letter to advisors (maxime.escourrou@laas.fr and ariane.herbulot@laas.fr). After reviewing these documents, a possible interview will be offered to the candidate so that he or she can present his or her experiences and motivation for the subject, as well as a discussion on the subject. The interview may be held in French or English, at the candidate's choice. Applications will be studied on a rolling basis.