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Volume: 29 | Article ID: art00007
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A Coarse-To-Fine Framework For Video Object Segmentation
  DOI :  10.2352/ISSN.2470-1173.2017.2.VIPC-403  Published OnlineJanuary 2017
Abstract

In this study, we develop an unsupervised coarse-to-fine video analysis framework and prototype system to extract a salient object in a video sequence. This framework starts from tracking grid-sampled points along temporal frames, typically using KLT tracking method. The tracking points could be divided into several groups due to their inconsistent movements. At the same time, the SLIC algorithm is extended into 3D space to generate supervoxels. Coarse segmentation is achieved by combining the categorized tracking points and supervoxels of the corresponding frame in the video sequence. Finally, a graph-based fine segmentation algorithm is used to extract the moving object in the scene. Experimental results reveal that this method outperforms the previous approaches in terms of accuracy and robustness.

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Chi Zhang, Alexander Loui, "A Coarse-To-Fine Framework For Video Object Segmentationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Visual Information Processing and Communication VIII,  2017,  pp 32 - 37,  https://doi.org/10.2352/ISSN.2470-1173.2017.2.VIPC-403

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
2470-1173
Society for Imaging Science and Technology