Scale-Aware Crowd Count Network with Annotation Error Correction
Traditional crowd counting networks suffer from information loss when feature maps are downsized through pooling layers, leading to inaccuracies in counting crowds at a distance. Existing methods often assume correct annotations during training, disregarding the impact of noisy annotations, especial...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
27.12.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Traditional crowd counting networks suffer from information loss when feature
maps are downsized through pooling layers, leading to inaccuracies in counting
crowds at a distance. Existing methods often assume correct annotations during
training, disregarding the impact of noisy annotations, especially in crowded
scenes. Furthermore, the use of a fixed Gaussian kernel fails to account for
the varying pixel distribution with respect to the camera distance. To overcome
these challenges, we propose a Scale-Aware Crowd Counting Network (SACC-Net)
that introduces a ``scale-aware'' architecture with error-correcting
capabilities of noisy annotations. For the first time, we {\bf simultaneously}
model labeling errors (mean) and scale variations (variance) by
spatially-varying Gaussian distributions to produce fine-grained heat maps for
crowd counting. Furthermore, the proposed adaptive Gaussian kernel variance
enables the model to learn dynamically with a low-rank approximation, leading
to improved convergence efficiency with comparable accuracy. The performance of
SACC-Net is extensively evaluated on four public datasets: UCF-QNRF, UCF CC 50,
NWPU, and ShanghaiTech A-B. Experimental results demonstrate that SACC-Net
outperforms all state-of-the-art methods, validating its effectiveness in
achieving superior crowd counting accuracy. |
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DOI: | 10.48550/arxiv.2312.16771 |