The paper introduces the LTS method for moving object segmentation, emphasizing the importance of scene independence and spatial correlation. The proposed approach outperforms state-of-the-art methods on various datasets, showcasing its potential as a general solution for real-world applications.
The LTS method consists of two networks: DIDL for temporal distribution learning and SBR for spatial correlation refinement. By addressing challenges like computational cost and overfitting, LTS offers an efficient and accurate solution for moving object segmentation.
Key contributions include the DIDL network for efficient learning of temporal distributions, an improved product distribution layer to enhance accuracy, and the SBR network to refine spatial correlations. Extensive experiments demonstrate the superiority of LTS over existing methods.
The study highlights the significance of spatial correlation in refining binary masks generated by the DIDL network. By combining temporal distribution learning with spatial correlation refinement, LTS achieves impressive results across diverse natural scenes.
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by Guanfang Don... at arxiv.org 03-07-2024
https://arxiv.org/pdf/2304.09949.pdfDeeper Inquiries