文章基本信息

标题：Efficient Video Denoising and Real Time Object Tracking Using Particle Filter: A Condensation Approach
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作者：Dr Samidha Dwivedi Sharma ; Amit H Palve
期刊名称：International Journal of Computer Technology and Applications
电子版ISSN：2229-6093
出版年度：2013
卷号：4
期号：1
页码：72-77
出版社：Technopark Publications
摘要：The video sequences are often corrupted by noise during acquisition and processing. The noise degrades the visual quality and also affects the efficiency of further processing like compression, segmentation etc... Hence, it becomes very important to remove the noise while preserving the original video contents.Tracking moving objects in video sequences is a central concern in computer vision. Reliable visual tracking is indispensable in many emerging vision applications such as automatic video surveillance, human–computer interfaces and robotics.Traditionally, the tracking problem is formulated as sequential recursive estimation having an estimate of the probability distribution of the target in the previous frame, the problem is to estimate the target distribution in the new frame using all available prior knowledge and the new information brought by the new frame. The Kalman filter provides an effective solution to the linear-Gaussian filtering problem.However, where there is nonlinearity, either in the model specification or the observation process, other methods is required. We consider methods known generically as particle filters, which include the condensation algorithm and the Bayesian bootstrap orsampling importance resampling (SIR) filter. In this paper we propose a particle filter for efficient video denoising as well as to track real time visual objects. As particle filter has many algorithms, we are using a condensation approach to implement the proposed system
关键词：Video denoising; Object Tracking; Kalman Filter; Linear-Gaussian Filter nonlinearity; Particle Filters; Bayesian bootstrap; Condensation.