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Recently the crowd counting has received more and more attention. Especiallythe technology of high-density environment has become an important researchcontent, and the relevant methods for the existence of extremely dense crowdare not optimal. In this paper, we propose a multi-level attentiveConvolutional Neural Network (MLAttnCNN) for crowd counting. We extracthigh-level contextual information with multiple different scales applied inpooling, and use multi-level attention modules to enrich the characteristics atdifferent layers to achieve more efficient multi-scale feature fusion, which isable to be used to generate a more accurate density map with dilatedconvolutions and a $1\times 1$ convolution. The extensive experiments on threeavailable public datasets show that our proposed network achievesoutperformance to the state-of-the-art approaches.

Multi-Level Attentive Convoluntional Neural Network for Crowd Counting