Scale-Aware Crowd Count Network with Annotation Error Correction
Yi-Kuan Hsieh Jun-Wei Hsieh Yu-Chee Tseng Ming-Ching Chang Li Xin
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.