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Deformable channel non‐local network for crowd counting
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Deformable channel non‐local network for crowd counting
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Journal Article
Deformable channel non‐local network for crowd counting
2023
Overview
Both global dependency and local correlation are crucial for solving the scale variation of crowd. However, most of previous methods fail to take two factors into consideration simultaneously. Against the aforementioned issue, a deformable channel non‐local network, abbreviated as DCNLNet for crowd counting, which can simultaneously learn global context information and adaptive local receptive field is proposed. Specifically, the proposed DCNLNet consists of two well‐crafted designed modules: deformable channel non‐local block (DCNL) and spatial attention feature fusion block (SAFF). The DCNL encodes long‐range dependencies between pixels and the adaptive local correlation with channel non‐local and deformable convolution, respectively, benefiting for improving the spatial discrimination of features. While the SAFF aims to aggregate the cross‐level information, which interacts these features from different depths and learns specific weights for the feature maps with spatial attention. Extensive experiments are performed on three crowd counting benchmark datasets and experimental results indicate that the proposed DCNLNet achieves compelling performance compared to other representative counting models. In the letter, a deformable channel non‐local network (DCNLNet) has been proposed for crowd counting. In order to explore global and local information, we develop a deformable channel non‐local module, which contains two branches, deformable convolution branch and channel non‐local branch, to learn adaptive local correlation and long‐range dependency. Moreover, we introduce a spatial attention feature fusion module to aggregate cross‐level features obtained from the encoder and the decoder.
Publisher
John Wiley & Sons, Inc,Wiley
