Regional Video Object Segmentation by Efficient Motion-Aware Mask Propagation
Date
2023
Authors
Miao, B.
Bennamoun, M.
Gao, Y.
Mian, A.
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Conference paper
Citation
Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2022), 2023, pp.1-6
Statement of Responsibility
Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
Conference Name
International Conference on Digital Image Computing: Techniques and Applications (DICTA) (30 Nov 2022 - 2 Dec 2022 : Sydney, Australia & virtual online)
Abstract
The use of optical flow to aid feature matching has been employed in recent self-supervised video object segmentation (VOS) methods and has shown promising results. However, computing pixel-wise optical flow is costly, and the optical flow can also be further utilized for efficient regional segmentation. To address these challenges, we propose an efficient motionaware mask propagation approach, dubbed EMMP, for selfsupervised VOS. EMMP introduces an efficient patch optical flow to compute the motion offsets of image patches for dynamic matching ROI generation. Fine-grained pixel-wise feature matching is performed based on the dynamic matching ROIs for mask propagation. To reduce redundant segmentation while avoiding unnecessary computations, we re-use the patch optical flow to estimate reliable foreground ROIs in the next frame and perform regional segmentation. Evaluation on benchmark VOS datasets shows that EMMP achieves competitive performance with significant wall-clock speed-ups compared to existing selfsupervised training methods, e.g., EMMP slightly outperforms MAMP and runs about 2× faster on segmentation. In addition, EMMP performs on par with many supervised training methods.
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© 2022 IEEE