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Bilateral counting network for single-image object counting

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Abstract

This paper proposes a novel bilateral counting network to estimate the accurate and robust counting result for single-image object counting task. The proposed network is composed of two main components: the concentrated dilated pyramid module and dual-context extraction path. The concentrated dilated pyramid module extracts the multi-scale feature from the image to address the scale variant issue in object counting task via a pyramid structure and also uses a shortcut concentration to facilitate the back-propagation of the gradient so as to improve the counting performance. And the dual-context extraction path obtains different-level context related to the object counting task through convoluting and down-sampling the image different times. The concentrated dilated pyramid module and the dual-context extraction path are integrated to boost the final counting result. Extensive experiments on vehicle counting and crowd counting datasets including TRANCOS, Mall, Shanghaitech_A and WorldExpo’10 demonstrate the feasibility and effectiveness for the object counting task.

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Acknowledgements

This work was supported partly by the National Natural Science Foundation of China (No. 61379065), the Natural Science Foundation of Hebei province in China (Nos. F2019203285; 2019203526), the Project funded by China Postdoctoral Science Foundation (No. 2018M631763) and Yanshan University Doctoral Foundation (BL18010)

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    He Li, Shihui Zhang & Weihang Kong

  2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, Hebei, China

    Shihui Zhang

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  1. He Li
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  2. Shihui Zhang
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Correspondence to Shihui Zhang.

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Li, H., Zhang, S. & Kong, W. Bilateral counting network for single-image object counting. Vis Comput 36, 1693–1704 (2020). https://doi.org/10.1007/s00371-019-01769-5

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