Video matting based on local-global features fusion

Niuniu Dong; Yihui Liang; Kun Zou; Wensheng Li; Fujian Feng

doi:10.1117/12.3029351

22 May 2024 Video matting based on local-global features fusion

Niuniu Dong, Yihui Liang, Kun Zou, Wensheng Li, Fujian Feng

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762K (2024) https://doi.org/10.1117/12.3029351
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Niuniu Dong, Yihui Liang, Kun Zou, Wensheng Li, and Fujian Feng "Video matting based on local-global features fusion", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762K (22 May 2024); https://doi.org/10.1117/12.3029351

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