Multi-granularity convolutional network for crowd counting

Yang Hu; Jinyuan Li; Wei Wang; Tong Li

doi:10.1117/12.2557192

3 January 2020 Multi-granularity convolutional network for crowd counting

Yang Hu, Jinyuan Li, Wei Wang, Tong Li

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731N (2020) https://doi.org/10.1117/12.2557192
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

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.

Citation Download Citation

Yang Hu, Jinyuan Li, Wei Wang, and Tong Li "Multi-granularity convolutional network for crowd counting", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731N (3 January 2020); https://doi.org/10.1117/12.2557192

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