Attention-guided feature fusion network for crowd counting

Qing He, Qianqian Yang, Yinfeng Xia, Sifan Peng, B. Yin
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Abstract

How to solve the scale variation and background interference faced by crowd counting algorithms in practical applications is still an open problem. In this paper, to tackle the above problems, we propose the Attention-guided Feature Fusion Network (AFFNet) to learn the mapping between the crowd image and density map. In this network, the Channel-attentive Receptive Field Block (CRFB) is constructed by parallel convolutional layers with different expansion rates to extract multi-scale features. By adopting attention masks generated by high-level features to adjust low-level features, the Feature Fusion Module (FFM) can alleviate the background interference problem at the feature level. In addition, the Double Branch Module (DBM) generates a density estimation map, which further erases the background interference problem at the density level. Extensive experiments conducted on several challenging benchmark datasets including ShanghaiTech, UCF-QNRF and JHU-CROWD++ demonstrate our proposed method is superior to the state-of-the-art approaches.
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用于人群计数的注意引导特征融合网络
如何解决实际应用中人群计数算法所面临的尺度变化和背景干扰问题仍然是一个有待解决的问题。为了解决上述问题,本文提出了一种注意力引导特征融合网络(AFFNet)来学习人群图像与密度图之间的映射。在该网络中,通道关注的感受野块(CRFB)由不同扩展率的并行卷积层构成,以提取多尺度特征。特征融合模块(Feature Fusion Module, FFM)通过高级特征生成的注意掩模来调节低级特征,缓解特征级背景干扰问题。此外,双分支模块(DBM)生成密度估计图,进一步消除了密度水平上的背景干扰问题。在包括上海科技、UCF-QNRF和JHU-CROWD++在内的几个具有挑战性的基准数据集上进行的大量实验表明,我们提出的方法优于最先进的方法。
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