On-line video multi-object segmentation based on skeleton model and occlusion detection

On-line video multi-object segmentation based on skeleton model and occlusion detection Multimed Tools Appl https://doi.org/10.1007/s11042-018-6208-x On-line video multi-object segmentation based on skeleton model and occlusion detection 1 2 Guoheng Huang & Chi-Man Pun Received: 11 October 2017 /Revised: 30 April 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this work, we propose an approach for on-line video multi object segmentation based on skeleton model and occlusion detection. We consider the multi-object segmentation in every frame as a multi-class region merging based object segmentation. We then generate the initial object superpixels automatically using a skeleton model from the second frame. Moreover, we also propose an initial background superpixel prediction scheme. In case the occlusion to affect the final segmentation result, we propose an occlusion detection model based on optical flow. The experimental results show that our method is both robust in segmenting multi objects and efficient in execution time. . . . . Keywords Multi-object segmentation Skeleton model Occlusion detection Superpixel On- line 1 Introduction The target of video object segmentation (VOS) is to extract the primary object from video clips and to eliminate the background in all frames. Video object segmentation is one of the hottest research topics in video processing, and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

On-line video multi-object segmentation based on skeleton model and occlusion detection

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6208-x
Publisher site
See Article on Publisher Site

Abstract

Multimed Tools Appl https://doi.org/10.1007/s11042-018-6208-x On-line video multi-object segmentation based on skeleton model and occlusion detection 1 2 Guoheng Huang & Chi-Man Pun Received: 11 October 2017 /Revised: 30 April 2018 /Accepted: 23 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract In this work, we propose an approach for on-line video multi object segmentation based on skeleton model and occlusion detection. We consider the multi-object segmentation in every frame as a multi-class region merging based object segmentation. We then generate the initial object superpixels automatically using a skeleton model from the second frame. Moreover, we also propose an initial background superpixel prediction scheme. In case the occlusion to affect the final segmentation result, we propose an occlusion detection model based on optical flow. The experimental results show that our method is both robust in segmenting multi objects and efficient in execution time. . . . . Keywords Multi-object segmentation Skeleton model Occlusion detection Superpixel On- line 1 Introduction The target of video object segmentation (VOS) is to extract the primary object from video clips and to eliminate the background in all frames. Video object segmentation is one of the hottest research topics in video processing, and

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 5, 2018

References

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