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Attention Based Quick Network With Optical Flow Estimation for Semantic Segmentation
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DOI：10.1109/ACCESS.2023.3241638.png)
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Abstract
En 中文
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.
Keywords:
Semantic segmentation
Optical imaging
Optical network units
Feature extraction
Optical propagation
Deep learning
Adaptation models
Video recording
deep learning
video processing