Authors:
Dubravko Culibrk
;
Oge Marques
;
Daniel Socek
;
Hari Kalva
and
Borko Furht
Affiliation:
Florida Atlantic University, United States
Keyword(s):
Video processing, Object segmentation, Background modeling, Bayesian modeling, Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Segmentation and Grouping
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Statistical Approach
;
Theory and Methods
Abstract:
Object segmentation from a video stream is an essential task in video processing and forms the foundation
of scene understanding, object-based video encoding (e.g. MPEG4), and various surveillance and2D-to-pseudo-3D conversion applications. The task is difficult and exacerbated by the advances in video capture and storage. Increased resolution of the sequences requires development of new, more efficient algorithms for object detection and segmentation. The paper presents a novel neural network based approach to background modeling for motion based object segmentation in video sequences. The proposed approach is designed to enable efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real time segmentation of high-resolution sequences.