Journal
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021
Volume -, Issue -, Pages 3674-3683Publisher
IEEE
DOI: 10.1109/WACV48630.2021.00372
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This paper introduces a novel method CFANet which generates high-quality crowd density maps and people count estimation by incorporating attention maps to better focus on the crowd area. The method utilizes a progressive attention mechanism to suppress irrelevant background and assign attention weights based on crowd density levels, achieving superior performance in count accuracy and image quality improvement. Through multi-level supervision and a Background-aware Structural Loss, the method also reduces false recognition ratio while enhancing structural similarity to groundtruth.
In this paper, we present a novel method Coarse- and Fine-grained Attention Network (CFANet) for generating high-quality crowd density maps and people count estimation by incorporating attention maps to better focus on the crowd area. We devise a from-coarse-to-fine progressive attention mechanism by integrating Crowd Region Recognizer (CRR) and Density Level Estimator (DLE) branch, which can suppress the influence of irrelevant background and assign attention weights according to the crowd density levels, because generating accurate fine-grained attention maps directly is normally difficult. We also employ a multi-level supervision mechanism to assist the backpropagation of gradient and reduce overfitting. Besides, we propose a Background-aware Structural Loss (BSL) to reduce the false recognition ratio while improving the structural similarity to groundtruth. Extensive experiments on commonly used datasets show that our method can not only outperform previous state-of-the-art methods in terms of count accuracy but also improve the image quality of density maps as well as reduce the false recognition ratio.
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