Title :
Corner-based background segmentation using Adaptive Resonance Theory
Author :
Maludrottu, S. ; Regazzoni, C.S. ; Sallam, H. ; Talkhan, I. ; Atiya, A.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Univ. of Genoa, Genova, Italy
Abstract :
A correct video segmentation, namely the detection of moving objects within a scene plays a very important role in many application in safety, surveillance, traffic monitoring and object detection. The main objective of this paper is to implement an effective background segmentation algorithm for corner sets extracted from video sequences. A dynamic prototype of the structure of background corners is produced and incoming corners are classified using a fuzzy ARTMAP neural network and labeled as pertaining to the background or foreground using a spatial clustering method. Finally the accuracy of the proposed algorithm is evaluated using PETS2006 benchmark data.
Keywords :
ART neural nets; feature extraction; fuzzy neural nets; image segmentation; image sequences; object detection; pattern classification; PETS2006 benchmark data; adaptive resonance theory; corner set extraction; corner-based background segmentation algorithm; fuzzy ARTMAP neural network; moving object detection; pattern classification; spatial clustering method; traffic monitoring; video segmentation; video sequences; Clustering algorithms; Layout; Monitoring; Object detection; Prototypes; Resonance; Safety; Surveillance; Telecommunication traffic; Video sequences; ARTMAP; Background segmentation; corner labeling;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5414379
