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Background subtraction in people detection framework for RGB-D cameras

Anh-Tuan Nghiem 1 François Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this paper, we propose a background subtraction algorithm specific for depth videos from RGB-D cameras. Embedded in a people detection framework, it does not classify foreground / background at pixel level but provides useful information for the framework to remove noise. Noise is only removed when the framework has all the information from background subtraction, classification and object tracking. In our experiment, our background subtraction algorithm outperforms GMM, a popular background subtraction algorithm, in detecting people and removing noise.
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Contributor : Soumik Mallick <>
Submitted on : Wednesday, July 25, 2018 - 4:59:55 PM
Last modification on : Thursday, July 26, 2018 - 1:16:47 AM
Long-term archiving on: : Friday, October 26, 2018 - 4:08:48 PM


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  • HAL Id : hal-01849217, version 1



Anh-Tuan Nghiem, François Bremond. Background subtraction in people detection framework for RGB-D cameras. AVSS, Aug 2014, Seoul, South Korea. ⟨hal-01849217⟩



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