Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting

Gang Lv, Yushan Xu, Zuchang Ma, Yining Sun and Fudong Nian
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Gang Lv: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Yushan Xu: School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China
Zuchang Ma: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Yining Sun: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Fudong Nian: School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China

Mathematics, 2022, vol. 10, issue 7, 1-17

Abstract: This paper attacks the two challenging problems of image-based crowd counting, that is, scale variation and complex background. To that end, we present a novel crowd counting method, called the Scale and Background aware Asymmetric Bilateral Network (SBAB-Net), which is able to handle scale variation and background noise in a unified framework. Specifically, the proposed SBAB-Net contains three main components, a pre-trained backbone convolutional neural network (CNN) as the feature extractor and two asymmetric branches to generate a density map. These two asymmetric branches have different structures and use features from different semantic layers. One branch is densely connected stacked dilated convolution (DCSDC) sub-network with different dilation rates, which relies on one deep feature layer and can handle scale variation. The other branch is parameter-free densely connected stacked pooling (DCSP) sub-network with various pooling kernels and strides, which relies on shallow feature and can fuse features with several receptive fields to reduce the impact of background noise. Two sub-networks are fused by the attention mechanism to generate the final density map. Extensive experimental results on three widely-used benchmark datasets have demonstrated the effectiveness and superiority of our proposed method: (1) We achieve competitive counting performance compared to state-of-the-art methods; (2) Compared with baseline, the MAE and MSE are decreased by at least 6.3 % and 11.3 % , respectively.

Keywords: crowd counting; asymmetric structure; bilateral network; density estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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