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Crowd counting via learning perspective for multi-scale multi-view Web images

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Abstract

Estimating the number of people in Web images still remains a challenging problem owing to the perspective variation, different views, and diverse backgrounds. Existing deep learning models still have difficulties in dealing with scenarios where the size of a person is either extremely large or extremely small. In this paper, we propose a novel perspective-aware architecture to estimate the number of people in a crowd in web images. Specifically, we use a two-stage framework, where we first learn a policy network to infer the perspective of the target scene, which outputs a scale label for the subsequent perspective normalization. Next, given the aligned inputs, we further adjust the scale-specific counting network to regress the final count. Experiments on challenging datasets demonstrate our approach can deal with a large perspective variation and that we have achieved state-of-theart results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61521002).

Author information

Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Chong Shang & Haizhou Ai

  2. Huawei Technologies, Beijing, 100084, China

    Yi Yang

Authors
  1. Chong Shang
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  2. Haizhou Ai
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  3. Yi Yang
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Corresponding author

Correspondence to Haizhou Ai.

Additional information

Chong Shang received the BS degree in computer science and technology with the honor of the Outstanding Graduating Student from Northwestern Polytechnical University, China in 2013. He is currently pursuing his PhD degree at Tsinghua University, China. His research interests are computer vision and deep learning, with a current specific focus on object detection and crowd analysis.

Haizhou Ai received the BS, MS, and PhD degrees from Tsinghua University, China in 1985, 1988, and 1991, respectively. From 1994 to 1996, he was with the Flexible Production System Laboratory, University of Brussels, Belgium, as a Postdoctoral Researcher. He is currently a Professor with the Computer Science and Technology Department, Tsinghua University. His research domain is in the computer vision and pattern recognition field, particularly in object detection, tracking, and recognition. He has published more than 80 papers in refereed journals and conference proceedings. He supervised the Best PhD Dissertation of the Beijing Municipal City in computer science and technology in the year of 2008 and the Best Student Paper of IEEE CVPR 2007.

Yi Yang received the BS degree in network engineering and PhD degree in pattern recognition from Sichuan University, China and the Institute of Automation, Chinese Academy of Sciences, China in 2010 and 2016, respectively. Since 2016, she has been with 2012 labs, Huawei Technologies Co., Ltd., China, where she is currently an algorithm engineer. Her research interests include computer vision, pattern recognition, deep learning, and object detection.

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Shang, C., Ai, H. & Yang, Y. Crowd counting via learning perspective for multi-scale multi-view Web images. Front. Comput. Sci. 13, 579–587 (2019). https://doi.org/10.1007/s11704-017-6598-3

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  • DOI: https://doi.org/10.1007/s11704-017-6598-3

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