DSGC-Net: A Dual-Stream Graph Convolutional Network for Crowd Counting via Feature Correlation Mining
This addresses crowd counting accuracy for applications like surveillance and public safety, but it is incremental as it builds on existing deep learning methods with a novel dual-stream approach.
The paper tackles the problem of crowd counting in complex scenarios with density distribution differences and inconsistent individual representations by proposing DSGC-Net, a dual-stream graph convolutional network that mines feature correlations, achieving MAE of 48.9 and 5.9 on ShanghaiTech Part A and Part B datasets, outperforming state-of-the-art methods.
Deep learning-based crowd counting methods have achieved remarkable progress in recent years. However, in complex crowd scenarios, existing models still face challenges when adapting to significant density distribution differences between regions. Additionally, the inconsistency of individual representations caused by viewpoint changes and body posture differences further limits the counting accuracy of the models. To address these challenges, we propose DSGC-Net, a Dual-Stream Graph Convolutional Network based on feature correlation mining. DSGC-Net introduces a Density Approximation (DA) branch and a Representation Approximation (RA) branch. By modeling two semantic graphs, it captures the potential feature correlations in density variations and representation distributions. The DA branch incorporates a density prediction module that generates the density distribution map, and constructs a density-driven semantic graph based on density similarity. The RA branch establishes a representation-driven semantic graph by computing global representation similarity. Then, graph convolutional networks are applied to the two semantic graphs separately to model the latent semantic relationships, which enhance the model's ability to adapt to density variations and improve counting accuracy in multi-view and multi-pose scenarios. Extensive experiments on three widely used datasets demonstrate that DSGC-Net outperforms current state-of-the-art methods. In particular, we achieve MAE of 48.9 and 5.9 in ShanghaiTech Part A and Part B datasets, respectively. The released code is available at: https://github.com/Wu-eon/CrowdCounting-DSGCNet.