Application :

For more information and application, please contact before April 29 th , 2018:
Josiane ZERUBIA, Inria-SAM
http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html
email: Josiane.Zerubia@inria.fr

Title: Stochastic Geometry for Multiple Object Detection and Tracking in
High Resolution Multi-Source Data Sets for Wide Area Surveillance Applications

Abstract:
Unmanned aerial vehicles and low-orbit satellites, including cubesats, are
increasingly used for wide area surveillance which results in large amounts of multi-
source data (videos) that have to be processed and analyzed. These sensor
platforms capture vast ground areas at roughly 2 frames per second. The number of
moving objects in such data is typically very high, accounting for up to thousands of
objects. Multiple objects tracking has traditionally been a major area of research in
the computer vision field, but this type of data poses new, specific, tracking related
challenges. The large number of small objects coupled with the reduced frame rate of
the video, illumination changes and image registration provide significant sources of
errors. Numerous motion models and state estimation methods like the Kalman filter
or the particle filter have been proposed for object tracking.
Classical trackers such as the Multiple Hypothesis Tracker or the Joint Probabilistic
Data Association Filter have been employed to solve the data association problem
between multiple detections. Both approaches work on a set of data association
hypothesis. A strong limitation of these methods is that past decisions cannot be
updated when new information is available. One way to cope with this problem is to
use a sliding temporal window to perform tracking taking into account both past and
future information and hence, removing the causality of the result. Recently, a new
spatio-temporal marked point process model specifically adapted to the problem of
multiple objects tracking has been developed by Craciun et al. [1]. Craciun et al. use
ellipses to model the objects, boats or cars for instance, adding a non-geometric
mark to facilitate the association between objects in different frames.

Nevertheless one important drawback of the above-mentioned model is that constant
velocity of the moving objects is a necessary prior hypothesis to deal with the
corresponding density function to be optimized.
In this PhD thesis we propose to get rid of this constraint by extending the previous
model of Paula Craciun .
[1] P. Craciun, M. Ortner, and J. Zerubia. Joint detection and tracking of moving objects
using spatio-temporal marked point processes. Proc. IEEE Winter Conference on
Applications of Computer Vision, 2015.

Candidate profile:
We encourage applications from outstanding candidates with strong academic
backgrounds in Mathematics, Physics, Computer Science, Engineering and related
fields. At Inria we seek to increase the number of women in areas where they are
underrepresented and therefore explicitly encourage women to apply. Furthermore,
we are committed to increasing the number of individuals with disabilities in its
workforce and therefore encourage applications from such qualified individuals.
Important notice: to get PhD funding from DGA it is necessary to be a
European citizen (EU and associated countries).