Attention guided feature pyramid network for crowd counting

Abstract Crowd counting has become a hot topic because of its wide applications in video surveillance and public security. However, one main problem of the deep learning methods for crowd counting is that the location information about the crowd is degraded irreversibly due to the spatial down-sampling of convolutional neural networks, which degrades the quality of generated density maps. To remedy the above problem, we propose an attention guided feature pyramid network (AG-FPN) for crowd counting, which can adaptively generate a high-quality density map with accurate spatial locations by combining the high- and low-level features. An attention block is added to each encoder layer to further emphasize the crowd regions and suppress the background clutters in feature extraction. Experimental results on the ShanghaiTech, UCF_CC_50, WorldExpo’10 and UCF-QNRF datasets demonstrate the superiority of the proposed method over state-of-the-art approaches.