Title :
GPU based GMM segmentation of kinect data
Author :
Amamra, Abdenour ; Mouats, Tarek ; Aouf, Nabil
Author_Institution :
Dept. of Inf. & Syst. Eng., Cranfield Univ., Shrivenham, UK
Abstract :
This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). We first start by the background subtraction from the colour and depth images separately. The foregrounds resulting from both streams are then fused for a more accurate detection. Our segmentation solution is implemented on the GPU. Thus, it works at the full frame rate of the sensor (30fps). Test results show its robustness against illumination change, shadows and reflections.
Keywords :
Gaussian processes; graphics processing units; image colour analysis; image segmentation; image sensors; mixture models; GPU-based GMM segmentation; Gaussian mixture models; Kinect data; RGBD data; RGBD sensors; background segmentation; background subtraction; colour images; depth images; foreground segmentation; Arrays; Graphics processing units; Image color analysis; Image segmentation; Lighting; Real-time systems; Robot sensing systems; Background substraction; Gaussian Mixture Models; RGBD sensors; image data fusion; real-time tracking;
Conference_Titel :
ELMAR (ELMAR), 2014 56th International Symposium
Conference_Location :
Zadar
DOI :
10.1109/ELMAR.2014.6923325