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Title	Multiscale Aggregation Network Via Smooth Inverse Map For Crowd Counting
ID_Doc	38647
Authors	Guo X.; Gao M.; Zhai W.; Li Q.; Pan J.; Zou G.
Year	2024
Published	Multimedia Tools and Applications, 83, 22
DOI	http://dx.doi.org/10.1007/s11042-022-13664-8
Abstract	Crowd counting is a practical yet essential research topic in computer vision, which has been beneficial to diverse applications in smart city environment safety. The commonly adopted paradigm in most existing methods is to regress a Gaussian density map that works as the learning objective during model training. However, given the unavoidable identity occlusion and scale variation in a crowd image, the corresponding Gaussian density map is degraded, failing to provide reliable supervision for optimization. To address this problem, we propose to replace the traditional Gaussian density map with a better alternation, namely the smooth inverse map (SIM). The proposed SIM can reflect the head location spatially and provide a smooth gradient to stabilize the model learning. Besides, we want the method to learn more discriminative features to cope with the challenge of large-scale variations. We deliver a multiscale aggregation (MA) to adaptively fuse features in different hierarchies to benefit semantic information under diverse receptive filed. The SIM and MA are meant to be complementary modules to guide the model in learning an accurate density map. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Author Keywords	Convolutional neural network; Crowd counting; Density map; Scale variation; Smart city

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