DocumentCode :
119751
Title :
Non parametric tool for vision detection analysis
Author :
Azzam, Riad ; Aouf, Nabil
Author_Institution :
Centre for Electron. Warfare, Cranfield Univ., Shrivenham, UK
fYear :
2014
fDate :
10-12 Sept. 2014
Firstpage :
1
Lastpage :
4
Abstract :
In this work, we deal with the problem of moving object detection using a non-parametric tool represented by the Gaussian process for classification. The technique used relies on the background subtraction approach for motion detection. In this context, a segmentation step is first implemented for pixel clustering before a binary Gaussian process classifier is applied to determine which pixel cluster those of news images belong to. The unclassified pixels are, therefore, labelled as detected targets. This proposed approach enables motion detection to be completed in a comparatively a short execution time with acceptable results. The results outlined here show the effectiveness of the approach to known background subtraction methods.
Keywords :
Gaussian processes; image classification; image motion analysis; image resolution; image segmentation; nonparametric statistics; object detection; pattern clustering; Gaussian process; background subtraction approach; binary Gaussian process classifier; motion detection; moving object detection problem; nonparametric tool; pixel clustering; target detection; vision detection analysis; Approximation methods; Clustering algorithms; Computational modeling; Computer vision; Gaussian processes; Image segmentation; Prototypes; Background Segmentation; Binary Gaussian Classifier; Gaussian Process; Vision detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ELMAR (ELMAR), 2014 56th International Symposium
Conference_Location :
Zadar
Type :
conf
DOI :
10.1109/ELMAR.2014.6923305
Filename :
6923305
Link To Document :
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