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Ph.D de

Ph.D
Group : Parallel Systems

Study and optimization of color object tracking algorithms

Starts on 01/10/2009
Advisor : ETIEMBLE, Daniel

Funding :
Affiliation : Université Paris-Sud
Laboratory : LRI PARALLELISME

Defended on 27/09/2013, committee :
Daniel Etiemble (Directeur de thèse), Université Paris Sud
Michèle Gouiffès (Co encadrante), Université Paris Sud
Séverine Dubuisson (Rapporteur), Université Paris 6
Michel Paindavoine (Rapporteur), Université de Bourgogne
Christine Fernandez-Maloigne (Examinatrice), Université de Poitiers
Joffroy Beauquier (Examinateur), Université Paris Sud

Research activities :

Abstract :
The work of this thesis focuses on the improvement and optimization of the color object
tracking algorithm Mean-Shift with both a strength point of view to improve the accuracy
and an architectural point of view to improve execution speed.

In this method, the tracked object is modeled by its color characteristics. The first
part of the work consisted in improving the robustness of the tracking. For this, the
impact of color space representation on the quality of tracking has been studied, and a
method for the selection of the color space that best represents the object to be tracked
has been proposed. The method has been coupled with a strategy determining the
appropriate time to recalculate the model. Color space selection method was also used in
collaboration with another object tracking algorithm - the covariance tracking - to
further improve the tracking robustness for particularly difficult sequences.

The final goal is to obtain a complete system to be implement on an embedded and real-time
operating system using commercial off-the-shelf product. Hardware targets are multi-core
processors SIMD.

Specific change have been made to the algorithm to better match the target architecture.
We use multiple parameters to customise their complexity and ensure they run in real time
on different platforms and various sizes of images or objects. Such compromise between
speed and performance makes real-time tracking possible on ARM processors like Cortex A9.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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