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.
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Acknowledgements
This work was supported in part by the Research Committee of the University of Macau under Grants MYRG2015-00011-FST and MYRG2015-00012-FST, the Science and Technology Development Fund of Macau SAR under Grants 093/2014/A2 and 041/2017/A1, and the project (2018 - 2020, Video Multi-object Co-segmentation Based on Superpixel, National Natural Science Foundation of China (NSFC) Grant No. 61702111).
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Huang, G., Pun, CM. On-line video multi-object segmentation based on skeleton model and occlusion detection. Multimed Tools Appl 77, 31313–31329 (2018). https://doi.org/10.1007/s11042-018-6208-x
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DOI: https://doi.org/10.1007/s11042-018-6208-x