Authors:
Antoine Vacavant
1
;
Lionel Robinault
2
;
Serge Miguet
2
;
Chris Poppe
3
and
Rik van de Walle
4
Affiliations:
1
Clermont Université, Université d’Auvergne and ISIT, France
;
2
Université de Lyon, CNRS and Université Lyon 2, France
;
3
Ghent University-IBBT, Belgium
;
4
Multimedia Lab - Elis - Ibbt - Ghent University, Belgium
Keyword(s):
H.264 Bitstream analysis, Background subtraction, Adaptive background modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Vision
;
Software Engineering
;
Statistical Approach
;
Tracking of People and Surveillance
;
Video Analysis
Abstract:
In this article, we propose a novel approach to detect moving objects in H.264 compressed bitstreams. More precisely, we describe a multi-modal background subtraction technique that uses the size of macroblocks in order to label them as belonging to the background of the observed scene or not. Here, we integrate an adaptive Gaussian mixture-based scheme to model the background. We evaluate our contribution using the PETS video dataset and a realist synthetic video sequence rendered by a 3-D urban environment simulator. We compare two different background models, and we show that the Gaussian mixture-based is the best and outperforms other techniques that use macro bloc sizes.