EHNet通过注意力机制提升人群计数与定位效率
EHNet: An Efficient Hybrid Network for Crowd Counting and Localization
- 将人群计数转为点回归，用空间注意力捕捉长程依赖
- 在四个数据集上优于现有方法，计算量更低
- 适合需要高效人群分析的安防与交通场景
近年来，人群计数与定位已成为计算机视觉的关键技术，应用广泛。单张图像中多尺度人群分布仍是主要挑战。为此，我们提出高效的混合网络（EHNet），用于高效人群计数与定位。通过将人群计数重构为点回归任务，EHNet利用空间位置注意力模块（SPAM）捕获全面的空间上下文与长程依赖。此外，设计自适应特征聚合模块（AFAM）以有效融合多尺度特征表示，并引入多尺度注意力解码器（MSAD）。在四个基准数据集上的实验表明，EHNet在计算开销更小的前提下达到具有竞争力的性能，在ShanghaiTech Part A、ShanghaiTech Part B、UCF-CC-50和UCF-QNRF上均优于现有方法。代码已公开于https://anonymous.4open.science/r/EHNet。
原文摘要 · Abstract (English)
In recent years, crowd counting and localization have become crucial techniques in computer vision, with applications spanning various domains. The presence of multi-scale crowd distributions within a single image remains a fundamental challenge in crowd counting tasks. To address these challenges, we introduce the Efficient Hybrid Network (EHNet), a novel framework for efficient crowd counting and localization. By reformulating crowd counting into a point regression framework, EHNet leverages the Spatial-Position Attention Module (SPAM) to capture comprehensive spatial contexts and long-range dependencies. Additionally, we develop an Adaptive Feature Aggregation Module (AFAM) to effectively fuse and harmonize multi-scale feature representations. Building upon these, we introduce the Multi-Scale Attentive Decoder (MSAD). Experimental results on four benchmark datasets demonstrate that EHNet achieves competitive performance with reduced computational overhead, outperforming existing methods on ShanghaiTech Part \_A, ShanghaiTech Part \_B, UCF-CC-50, and UCF-QNRF. Our code is in https://anonymous.4open.science/r/EHNet.
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