An Automatic Scale-Adaptive Approach With Attention Mechanism-Based Crowd Spatial Information for Crowd Counting

Abstract

This paper proposes an automatic scale-adaptive approach with attention mechanism-based crowd spatial information addressing the crowd counting task, i.e. a novel cascaded crowd counting network. The proposed network is composed of a classification sub-network to estimate crowd scales and the main network to predict the corresponding density maps. First, the image serves as the input of the classification network and the main network. Second, according to the estimated crowd scale results, the main network structure is adjusted; simultaneously, the feature in the intermediate layer of the classification network is added stepwise into the main crowd counting network. Then, the semantic feature of the classification network is converted into the crowd spatial information mask via the proposed spatial attention conversion module, and the crowd spatial information mask is weighted into the specific feature of the main network. Last, the crowd density map and the crowd counting result are obtained. The experiments on challenging Mall, the Shanghaitech_A and Shanghaitech_B datasets prove the effectiveness, feasibility, and robustness of the proposed method, and the ablation study demonstrates the structure rationality of the proposed network.