| Autor: |
Jiawen Cai, Yarong Liu, Pan Qin |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 12402-12413 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3241638 |
| Popis: |
Video semantic segmentation is a challenging vision task since the temporal-spatial characteristics are difficult to model to satisfy the requirements of real-time and accuracy simultaneously. To tackle this problem, this paper proposes a novel optical flow based method. We propose an adaptive threshold key frame scheduling strategy to model the temporal information by estimating the inter-frame similarity. To ensure segmentation accuracy, we construct a convolutional neural network named Quick Network with attention (QNet-attention), a lightweight image semantic segmentation model with a spatial-pyramid-pooling-attention module. The proposed network is further combined with optical flow estimation to realize a semantic segmentation framework. The performance of the proposed method is verified with existing benchmark methods. The experimental results indicated that our method achieves excellent balanced performance on accuracy and speed. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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