NDLI: A Novel Video Object Tracking Approach Based on Kernel Density Estimation and Markov Random Field

Content Provider	IEEE Xplore Digital Library
Author	Zhi Liu Liquan Shen Zhongmin Han Zhaoyang Zhang
Copyright Year	2007
Description	Author affiliation: Shanghai Univ., Shanghai (Zhi Liu; Liquan Shen; Zhongmin Han; Zhaoyang Zhang)
Abstract	In this paper, we propose a novel video object tracking approach based on kernel density estimation and Markov random field (MRF). The interested video objects are first segmented by the user, and a nonparametric model based on kernel density estimation is initialized for each video object and the remaining background, respectively. A temporal saliency map is also initialized for each object to memorize the temporal trajectory. Based on the probabilities evaluated on the non-parametric models, each pixel in the current frame is first classified into the corresponding video object or background using the maximum likelihood criterion. Starting from the initial classification result, a MRF model that combines spatial smoothness and temporal coherency is selectively exploited to generate more reliable video objects. The nonparametric model and the temporal saliency map for each video object are updated and propagated for the future tracking. Experimental results on several MPEG-4 test sequences demonstrate the good segmentation performance of our approach.
File Size	278034
File Format	PDF
ISBN	9781424414369
ISSN	15224880
DOI	10.1109/ICIP.2007.4379324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Markov random fields Object segmentation Context modeling Parametric statistics Displays Systems engineering education Region 9 Gaussian distribution Layout Markov random field Video object segmentation Video object tracking Kernel density estimation
Content Type	Text
Resource Type	Article

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