Title :
Automatic object detection based on adaptive background subtraction using symmetric alpha stable distribution
Author :
Bhaskar, Harish ; Mihaylova, Lyudmila ; Achim, Alin
Author_Institution :
Dept. of Commun. Syst., Lancaster Univ., Lancaster
Abstract :
Automatic detection of objects is critical to video tracking systems. One of the simplest techniques for detection is background subtraction (BS). BS refers to the process of segmenting moving regions from image sequences. The BS process involves building a model of the background and extracting regions of the foreground (moving objects). In this paper, we propose an extended cluster BS (CBS) technique based on symmetric alpha stable (SalphaS) distributions. The developed method functions at cluster-level as against the traditional pixel-level BS methods. An iterative self-adaptive mechanism is presented that allows automated learning of the distribution of the model parameters. The results for the CBS SalphaS algorithm on real video sequences show improvement compared with a CBS using a Gaussian mixture model.
Keywords :
Gaussian processes; image segmentation; image sequences; iterative methods; object detection; statistical distributions; video signal processing; Gaussian mixture model; adaptive background subtraction; automated learning; automatic object detection; image segmentation; image sequences; iterative self-adaptive mechanism; real video sequences; symmetric alpha stable distributions; video tracking systems;
Conference_Titel :
Target Tracking and Data Fusion: Algorithms and Applications, 2008 IET Seminar on
Conference_Location :
Birmingham
Print_ISBN :
978-0-86341-910-2