Scale-Aware Crowd Counting Network With Annotation Error Modeling.

Abstract

Traditional crowd-counting networks suffer from information loss when feature maps are reduced by 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, using a fixed Gaussian density model does not account for the varying pixel distribution of the camera distance. To overcome these challenges, we propose a Scale-Aware Crowd Counting Network (SACC-Net) that introduces a scale-aware loss function with error-compensation capabilities of noisy annotations. For the first time, we simultaneously model labeling errors (mean) and scale variations (variance) by spatially varying Gaussian distributions to produce fine-grained density maps for crowd counting. Furthermore, the proposed scale-aware Gaussian density model can be dynamically approximated with a low-rank approximation, leading to improved convergence efficiency with comparable accuracy. To create a smoother scale-aware feature space, this paper proposes a novel Synthetic Fusion Module (SFM) and an Intra-block Fusion Module (IFM) to generate fine-grained heat maps for better crowd counting. The lightweight version of our model, named SACC-LW, enhances the computational efficiency while retaining accuracy. The superiority and generalization properties of scale-aware loss function are extensively evaluated for different backbone architectures and performance metrics on six public datasets: UCF-QNRF, UCF CC 50, NWPU, ShanghaiTech A, ShanghaiTech B, and JHU. Experimental results also demonstrate that SACC-Net outperforms all state-of-the-art methods, validating its effectiveness in achieving superior crowd-counting accuracy. The source code is available at https://github.com/Naughty725.