Smart City Gnosys

Smart city article details

Title	A Spatio- Temporal Attentive Network For Video-Based Crowd Counting
ID_Doc	4875
Authors	Avvenuti M.; Bongiovanni M.; Ciampi L.; Falchi F.; Gennaro C.; Messina N.
Year	2022
Published	Proceedings - IEEE Symposium on Computers and Communications, 2022-June
DOI	http://dx.doi.org/10.1109/ISCC55528.2022.9913019
Abstract	Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer vision techniques rely on deep learning-based algorithms that estimate pedestrian densities in still, individual images. Only a bunch of works take advantage of temporal consistency in video sequences. In this work, we propose a spatio-temporal attentive neural network to estimate the number of pedestrians from surveillance videos. By taking advantage of the temporal correlation between consecutive frames, we lowered state-of-the-art count error by 5% and localization error by 7.5% on the widely-used FDST benchmark. © 2022 IEEE.
Author Keywords	Crowd Counting; Deep Learning; Smart Cities; Visual Counting

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