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Segmentation d'objets mobiles par fusion RGB-D et invariance colorimétrique

Abstract : This PhD thesis falls within the scope of video-surveillance, and more precisely focuses on the detection of movingobjects in image sequences. In many applications, good detection of moving objects is an indispensable prerequisiteto any treatment applied to these objects such as people or cars tracking, passengers counting, detection ofdangerous situations in specific environments (level crossings, pedestrian crossings, intersections, etc.), or controlof autonomous vehicles. The reliability of computer vision based systems require robustness against difficultconditions often caused by lighting conditions (day/night, shadows), weather conditions (rain, wind, snow...) and thetopology of the observed scene (occultation...).Works detailed in this PhD thesis aim at reducing the impact of illumination conditions by improving the quality of thedetection of mobile objects in indoor or outdoor environments and at any time of the day. Thus, we propose threestrategies working as a combination to improve the detection of moving objects:i) using colorimetric invariants and/or color spaces that provide invariant properties ;ii) using passive stereoscopic camera (in outdoor environments) and Microsoft Kinect active camera (in outdoorenvironments) in order to partially reconstruct the 3D environment, providing an additional dimension (a depthinformation) to the background/foreground subtraction algorithm ;iii) a new fusion algorithm based on fuzzy logic in order to combine color and depth information with a certain level ofuncertainty for the pixels classification.
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Submitted on : Friday, September 7, 2018 - 3:00:06 PM
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  • HAL Id : tel-01870372, version 1



Julian Murgia. Segmentation d'objets mobiles par fusion RGB-D et invariance colorimétrique. Autre. Université de Technologie de Belfort-Montbeliard, 2016. Français. ⟨NNT : 2016BELF0289⟩. ⟨tel-01870372⟩



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