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Offre de Thèse Institut Pascal : Three-dimensional particle tracking velocimetry (3DPTV) using a camera network

7 Septembre 2021

Catégorie : Doctorant

Thesis objectives

Thesis context
Selection process
Additional information

Thesis objectives:

The aim of this thesis is to define the mathematical foundations for the study of airflow in large cavities, such as atrium or conference rooms, by juxtaposition of several different 3DPTV systems. First, a procedure for multi-3DPTV multi-camera calibration will be developed in order to express all particle trajectories in a common 3D coordinate system. Then, a strategy to merge particle trajectories calculated by different 3DPTV systems will be studied basing on a similarity criterion and a merging criterion, both dependent on the camera extrinsic calibration, the frame rate and on each particle 3D trajectory.

Self-calibration of camera networks aims to jointly estimate intrinsic and extrinsic parameters of the cameras by using natural features, i.e. the same features present during the normal use of the network, instead of known patterns designed for the calibration task. In order to reduce the degree of expertise needed for the 3DPTV measuring operator, self-calibrating technique should be developed that enables the camera network to configure itself solely by using background geometrical structure, human activity detection and particle tracking. For efficiency reason, cameras generally form sparse networks with a limited (and sometimes an inexistent) overlap of the fields of view. Thus, the proposed technique should rely on using only pairwise point correspondences while most of existing methods use triple or more correspondences which are difficult to establish in the context of TRAQ, because of wide base-line or occlusion. Therefore, the trajectories of image features will be exploited to enforce the bundle adjustment with a smoothness constraint. This task usually requires that enough features are seen simultaneously by at least two cameras, which is challenging in our context because of the difficulty to find a sufficient number of correct matches due to the density and the similarity in the appearance of the features (a high false positives rate may occur), and the possible presence of some cameras that do not have overlapping fields of view. For this task, we will investigate two approaches. First, we will develop a spatiotemporal bundle adjustment technique. Unlike traditional bundle adjustment, the spatiotemporal bundle adjustment tries to jointly optimize 3D trajectories of dynamic points as well as static points when such points are detected in the background. Second, in order to boost the candidate matches generation, we will investigate the use of deep Convolutional Neural networks (CNN) for both tracking and matching generation steps. For example, this CNN can be used to efficiently weight the candidate pairs in order to pick the best ones. The use of Time-of-flight cameras among the network will also be studied. This kind of cameras gives directly the 3D position of the particles but are harder to calibrate.



Thesis context:


This thesis is a part of the project TRAQ (Three-dimensional tracking of monodisperse TRAjectories by Quantitative measurements) founded by ANR. In buildings and more generally in confined ventilated spaces such as aircraft cabins, it is important to be able to predict the trajectory and velocity of the air and of airborne pollutants and contaminants. The challenge is both environmental and energetic. On the environmental front, the stake is the control of indoor air quality, a public health issue. Indoor air is indeed loaded with particles emitted by human breathing and activity and by indoor furnitures. These particles can be harmful to the occupants. For example, many chemical compounds such as formaldehyde or carbon monoxide can be fatal. Predicting the trajectory of such airborne particles makes it possible to assess the exposure risks of building occupants, helps optimizing the ventilation strategies and even the evacuation of people in the event of a fire, or the emission of a dangerous gas. The current Covid-19 crisis is a stern reminder on how important it is to predict the motion of indoor contaminants. In terms of energy, predicting the trajectory and velocity of the air allows optimizing heating or cooling systems dedicated to indoor thermal comfort. For example, in low energy buildings, it can help choosing the shape and position of the heating devices and air diffusers offering the best coverage of the living area, and therefore to reach the best comfort at the lower cost.

The developed code will be experimentally validated in the CETHIL’s test cell MINIBAT, by comparing experimental results using multi-3DPTV with helium filled bubbles, to the experimental data obtained during the recent PhD thesis of Chi Ken Nguyen in the same facility using multiple point hot sphere anemometry. MINIBAT has the dimensions of a realistic indoor rectangular room. Its thermal boundary conditions are fully controlled and can reproduce the effect of adjacent rooms with fixed temperature along five faces of the test cell, and the effect of outdoor climatic conditions along the remaining face. It also possesses an air inlet with controllable velocity and temperature. The test cell was designed to allow a very fine characterization of the indoor thermal environment under controlled and reproducible boundary conditions. The validated algorithm will later be translated into low level language, and integrated in the real-time process.





Omar Ait Aider,

Ass. prof. Institut Pascal / EUPI, Tél. +334 73 40 55 67 – email

Pascal Biwolé,

Prof. Institut Pascal / IUT Montluçon, Tél. +334 70 02 20 25 – email



1580 € net (1970 brut), Sécurité sociale is fully included and free of charge. International PhD students can benefit from a financial aid for their rent, from the French state, and paid by the CAF (Caisse d’Allocations Familiales). Beware, this aid is subject to conditions and its amount partly depends on your financial resources


Selection process :

Please submit your application by email to both Prof. and

In your email, please include the following:

A brief motivation of your application: what do you consider the best facts in your CV, which demonstrate your academic excellence in BsC and/or Msc. education? What are your reasons to pursue a PhD? Why would you like to work at Université Clermont-Auvergne? ...

A detailed CV, describing your earlier experience and studies;

A list of publications (if available);

A transcript of your educational record (list of courses per year, number of obtained credits, obtained marks) if available. This need not be official document at this stage ;

A (rough) indication or estimate of your rank among other students (e.g., top 10% among 35 students in my master);

If available: 1-3 English language documents describing your earlier research (e.g., scientific papers, master thesis, report on project work, etc.).

These documents need not be on the topic of the positions.


Additional comments :

Institut Pascal, UMR 6602, is a joint interdisciplinary research and training unit of 370 people, under the threefold supervision of Université Clermont Auvergne (UCA), the CNRS and SIGMA Clermont. The CHU, University Hospital of Clermont-Ferrand, is also a partner of the laboratory. Institut Pascal was born from the merging of six laboratories encompassing Engineering and Systems Sciences: Process Engineering, Mechanics, Robotics, Physics for Information Sciences, Health. The aim was to structure the field of the Engineering Sciences of the site of Clermont-Ferrand. The process was achieved in two steps (in 2012, then in January 2017). The research unit develops knowledge and technologies that contribute to three areas of application: plants, transportation and the hospital of the future. Institut Pascal is a member of FACTOLAB, a joint laboratory with MICHELIN. It is owner of the LabEx IMobS3 (laboratory of excellence from the PIA1 French investment program for the future). Institut Pascal is member of the CNRS network EquipEx ROBOTEX and of the LabEx GaNeX (PIA1). The unit is also member of the competitiveness clusters Céréales Vallée and ViaMéca.

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