DA2Net: a dual attention-aware network for robust crowd counting

Crowd counting in congested scenes is a crucial yet challenging task in video surveillance and urban security system. The performance of crowd counting has been greatly boosted with the rapid development of deep learning. However, robust crowd counting in high-density environment with scale variations remains under-explored. To address this problem, we propose a dual attention-aware network ( DA 2 Net) for robust crowd counting in dense crowd scene with scale variations. Specifically, the DA 2 Net consists of two modules, namely Spatial Attention (SA) module and Channel Attention (CA) module. The SA module focuses on the spatial dependencies in the whole feature map to locate the heads accurately. The CA module attempts to handle the relations between channel maps and highlights the discriminative information in specific channels. Thus, it alleviates the mistaken estimation for background regions. The interactions between SA module and CA module provide the synergy which facilitates the learning of discriminative features with a focus on the essential head region. Experimental results on five benchmark datasets, i.e., ShanghaiTech, UCF_CC_50, UCF-QNRF, WorldExpo’10, and NWPU, demonstrate that the DA 2 Net can achieve the state-of-the-art performance on both accuracy and robustness.