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Design and Optimization of Crowd Behavior Analysis System Based on B/S Software Architecture

  • Yuanhang He
  • Jing Guo
  • Xiang Ji
  • Hua YangEmail author
Conference paper
  • 31 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)

Abstract

With the development of society and economy, the importance of crowd behavior analysis is increasing. However, the system often requires a large amount of computing resources, which is often difficult to meet for personal computers in traditional client/server architecture (C/S architecture). So based on the existing local analysis system [5], we construct a crowd behavior analysis system based on browser/server architecture (B/S architecture). Then we optimize many aspects of this B/S system to improve its communication capability and stability under high load. Finally, the acceleration work of the CGAN-based crowd counting module is carried out. The generator of CGAN (Conditional Generative Adversarial Network) was optimized such as residual layer pruning, upsampling optimization, and instance normalization layer removing, and then deployed and INT8 quantized in TensorRT. After these optimizations, the inferring speed on the NVIDIA platform is increased to 541.6% of the original network with almost no loss of inference accuracy.

Keywords

Crowd behavior analysis B/S architecture CGAN Network acceleration 

Notes

Acknowledgement

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, 18DZ1200102, 18DZ2270700), and SJTUYitu/Thinkforce Joint laboratory for visual computing and application. Director is funded by National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data PSRPC.

References

  1. 1.
    Gao, Y.: Intensive crowd counting algorithm based on conditional generative adversarial network (2018)Google Scholar
  2. 2.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  3. 3.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  4. 4.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)Google Scholar
  5. 5.
    Li, J.: Crowd analysis research for complex video surveillance scenes (2016)Google Scholar
  6. 6.
  7. 7.
    TehnoKV: Fusing batch normalization and convolution in runtime (2018). https://tehnokv.com/posts/fusing-batchnorm-and-conv
  8. 8.
    Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
  9. 9.
    Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)Google Scholar
  10. 10.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 2223–2232 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Institution of Image Communication and Network EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.National Engineering Laboratory for Public Security Risk Perception and Control by Big Data (PSRPC)China Academic of Electronics and Information TechnologyBeijingChina

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