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Multi-granularity convolutional network for crowd counting
Author(s): Yang Hu; Jinyuan Li; Wei Wang; Tong Li
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Paper Abstract

Estimating the number of people in images is an important task in computer vision, and has a wide range of applications, such as video surveillance, traffic monitoring, public safety and urban planning. The task of crowd counting faces many challenges, e.g. extremely scale variations in extremely dense crowd scenarios, severe occlusion, and perspective distortion. We have studied a new type of multi-granularity full convolutional network as an effective solution for crowd counting. We not only designed the parallel multi-receptive field module to learn the mapping from the crowd images to the density maps, but also introduced the skip-connection mechanism to better train the mapping to improve the quality of the estimated density map, which is critical for accurate crowd counting. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than the most advanced method.

Paper Details

Date Published: 3 January 2020
PDF: 7 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731N (3 January 2020); doi: 10.1117/12.2557192
Show Author Affiliations
Yang Hu, Yunnan Univ. (China)
Jinyuan Li, Yunnan Univ. (China)
Wei Wang, Yunnan Univ. (China)
Tong Li, Yunnan Univ. (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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