Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
This work addresses crowd counting for dense crowds, which is an incremental improvement over existing methods.
The paper tackles the problem of crowd counting in high-density environments by proposing a multi-level attentive convolutional neural network (MLAttnCNN), which achieves state-of-the-art performance on three public datasets.
Recently the crowd counting has received more and more attention. Especially the technology of high-density environment has become an important research content, and the relevant methods for the existence of extremely dense crowd are not optimal. In this paper, we propose a multi-level attentive Convolutional Neural Network (MLAttnCNN) for crowd counting. We extract high-level contextual information with multiple different scales applied in pooling, and use multi-level attention modules to enrich the characteristics at different layers to achieve more efficient multi-scale feature fusion, which is able to be used to generate a more accurate density map with dilated convolutions and a $1\times 1$ convolution. The extensive experiments on three available public datasets show that our proposed network achieves outperformance to the state-of-the-art approaches.