End to end multi-scale convolutional neural network for crowd counting

Deyi Ji; Hongtao Lu; Tongzhen Zhang

doi:10.1117/12.2522940

15 March 2019 End to end multi-scale convolutional neural network for crowd counting

Deyi Ji, Hongtao Lu, Tongzhen Zhang

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110412S (2019) https://doi.org/10.1117/12.2522940
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

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.

Citation Download Citation

Deyi Ji, Hongtao Lu, and Tongzhen Zhang "End to end multi-scale convolutional neural network for crowd counting", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412S (15 March 2019); https://doi.org/10.1117/12.2522940

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