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Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

PRCV 2019: Pattern Recognition and Computer Vision pp 429–440Cite as

Scalable Receptive Field GAN: An End-to-End Adversarial Learning Framework for Crowd Counting

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

Abstract

Crowd counting is challenging for unrestricted open outdoor and diverse scenes. To address large variety of perspective, density distribution and clutter problems, a novel end-to-end deep generative adversarial framework with scalable receptive field (SRFGAN) is proposed for obtaining high quality density estimation in this paper. Specifically, our generator adopts an encoder-decoder network with residual blocks to achieve multi-scale features due to scalable receptive fields which adapts to different scale crowd distribution. We also explore a spatial global pooling layer to acquire image-level prior representation which helps to tackle severe perspective distortion and background clutter. Besides, feature matching loss and adversarial loss are combined via a joint training scheme, which helps to improve the quality of generated density map. Experiment results on ShanghaiTech and UCF_CC_50 datasets illustrate the superior effectiveness.

The first author is a student.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (NSFC, Grant No. 61771303 and 61671289), Science and Technology Commission of Shanghai Municipality (STCSM, Grant Nos. 17DZ1205602, 18DZ1200-102, 18DZ2270700), SJTUYitu/Thinkforce Joint laboratory for visual computing and application, and National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC).

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Authors and Affiliations

  1. Institution of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yukang Gao & Hua Yang

Authors
  1. Yukang Gao
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  2. Hua Yang
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Corresponding author

Correspondence to Hua Yang .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Gao, Y., Yang, H. (2019). Scalable Receptive Field GAN: An End-to-End Adversarial Learning Framework for Crowd Counting. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_36

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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