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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xuchao Gong Zongmin Li |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. Comput. Sci. & Commun. Eng., China Univ. of Pet., Qingdao, China (Xuchao Gong; Zongmin Li) |
| Abstract | Efficient segmentation of foreground moving objects is an important procedure to achieve stable object tracking and recognition. In this paper, we have developed a novel algorithm for foreground segmentation that can extract moving objects from background accurately and efficiently. In our proposed algorithm, Gaussian Mixture Model is first used to model the static background regions. The boundary box of the foreground regions is determined via inter-frame change detection and SIFT feature analysis, and Grabcut algorithm(an image matting technology) is then used to obtain the optimal segmentation of foreground moving objects. Experiments on a set of video clips with huge diverse scenes have demonstrated the efficiency of our proposed method. |
| Sponsorship | Hubei Univ. Automot. Technol. |
| Starting Page | 750 |
| Ending Page | 753 |
| File Size | 3059782 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769550046 |
| DOI | 10.1109/ICCIS.2013.202 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Computer vision GrabCut Foreground segmentation SIFT Image color analysis Hidden Markov models Lighting Object segmentation Feature extraction HSV Gaussian Mixture Model (GMM) |
| Content Type | Text |
| Resource Type | Article |
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