Abstract
Although great progress has been made in crowd counting, accurate estimation of crowd numbers in high-density areas and full mitigation of the interference of background noise remain challenging. To address these issues, we propose a method called Double Attention Refinement Guided Counting Network (DARN). DARN introduces an attention-guided feature aggregation module that dynamically fuses features extracted from the Transformer backbone. By adaptively fusing features at different scales, this module can estimate the crowd for high-density areas by restoring the lost fine-grained information. Additionally, we propose a segmentation attention-guided refinement method with multiple stages. In this refinement process, crowd background noise is filtered by introducing segmentation attention maps as masks, resulting in a significant refinement of the foreground features. The introduction of multiple stages can further refine the features by utilizing fine-grained and global information. Extensive experiments were conducted on four challenging crowd counting datasets: ShanghaiTech A, UCF-QNRF, JHU-CROWD++, and NWPU-Crowd. The experimental results validate the effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China (61972059, 62376041, 42071438, 62102347), China Postdoctoral Science Foundation(2021M69236), Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (93K172021K01).
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Chang, S., Zhong, S., Zhou, L., Zhou, X., Gong, S. (2024). DARN: Crowd Counting Network Guided by Double Attention Refinement. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_37
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