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In this paper, we present a novel method Coarse- and Fine-grained AttentionNetwork (CFANet) for generating high-quality crowd density maps and peoplecount estimation by incorporating attention maps to better focus on the crowdarea. We devise a from-coarse-to-fine progressive attention mechanism byintegrating Crowd Region Recognizer (CRR) and Density Level Estimator (DLE)branch, which can suppress the influence of irrelevant background and assignattention weights according to the crowd density levels, because generatingaccurate fine-grained attention maps directly is normally difficult. We alsoemploy a multi-level supervision mechanism to assist the backpropagation ofgradient and reduce overfitting. Besides, we propose a Background-awareStructural Loss (BSL) to reduce the false recognition ratio while improving thestructural similarity to groundtruth. Extensive experiments on commonly useddatasets show that our method can not only outperform previous state-of-the-artmethods in terms of count accuracy but also improve the image quality ofdensity maps as well as reduce the false recognition ratio.

Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation