Neural network approach to background modeling for video object segmentation.
This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.
Florida Atlantic University, Boca Raton, FL 33431, USA. dculibrk@fau.edu
Dubravko
D
Culibrk
Oge
O
Marques
Daniel
D
Socek
Hari
H
Kalva
Borko
B
Furht
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003124:Colorimetry; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005246:Feedback; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D008029:Lighting; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*