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Proceedings Paper

End to end multi-scale convolutional neural network for crowd counting
Author(s): Deyi Ji; Hongtao Lu; Tongzhen Zhang
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Paper Abstract

Crowd counting is a challenging task in computer vison field and haven’t been well addressed until now. In this paper, we intend to develop an end to end multi-scale deep convolutional neural network(CNN) model that can accurately estimate the crowd count from an individual image with arbitrary crowd density and perspective. The proposed model extract multi-scale deep CNN features from the input image and regress the crwod count directly, without any post-processing . Hence our model could handle muti-scale targets well in various crowd scene. We evaluate our model on several benchmark datasets and the performance outperforms some state-of-the-art methods. What’s more, due to the end-to-end characteristics, our model demonstrates good practical application performance.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412S (15 March 2019); doi: 10.1117/12.2522940
Show Author Affiliations
Deyi Ji, Shanghai Jiao Tong Univ. (China)
Hongtao Lu, Shanghai Jiao Tong Univ. (China)
Tongzhen Zhang, Shanghai Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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