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Video Object Tracking based on Automatic Background Segmentation and updating using RBF neural network

Author(s): Pushpender Prasad Chaturvedi | Amit Singh Rajput | Aabha Jain

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 3;
Issue: 10;
Start page: 86;
Date: 2013;
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Keywords: V ideo Processing | W avelet | RBF

n this paper, the problems associated with theautomatic objectsegmentation of the videosequences are considered. Towards this objective, aunique method that combines of image and videoprocessing techniques ranged from noise filtering todata clustering is developed. The method alsoaddresses a number of challenging issues alongwith computational complexity, accuracy,generality, and robustness. One of the primary aimsof this paper is to find segmentation of color,texture, motion, shape, frame difference, and othermethods of video segmentation for automaticdetection considering the real-time processingrequirements. In contrast to frame-wise trackingtechniques, the employment of a spatiotemporaldata that is constructed from multiple video framesintroduces new degrees of freedom that can beexploited in termsof object extraction and contentanalysis. The current notions of regionsegmentation are extended to the spatiotemporaldomain, and new models to estimate the objectmotion are derived.
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