Advertisement

A Multi-view Deep Learning Approach for Detecting Threats on 3D Human Body

  • Zhicong Yan
  • Shuai Feng
  • Fangqi Li
  • Zhengwu Xu
  • Shenghong LiEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

Deep Neural Network-based methods have recently shown an outstanding performance on object detection tasks in 2D scenarios. But many tasks in real world requires object detection in 3D space. In order to narrow this gap, we investigate the task of detection and localization in 3D human body in this paper, and propose a multi-view-based deep learning approach to solve this issue. The experiments show that the proposed approach can effectively detect and locate specific stuff in 3D human body with high accuracy.

Keywords

Multi-view convolution neural network 3D object detection Airport security 

Notes

Acknowledgements

This research work is funded by the National Key Research and Development Project of China (2016YFB0801003) and the Sichuan province & university cooperation (Key Program) of science & technology department of Sichuan Province (2018JZ0050).

References

  1. 1.
    Elias, B.: Airport body scanners: the role of advanced imaging technology in airline passenger screening. Congressional Research Service, Library of Congress (2012)Google Scholar
  2. 2.
    Krizhevsky, A., Sutskever, I. and Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)Google Scholar
  3. 3.
    Su, H., et al.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 945–953 (2016)Google Scholar
  4. 4.
    Qi, C.R., et al.: Volumetric and multi-view cnns for object classification on 3d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5648–5656 (2016)Google Scholar
  5. 5.
    Qi, C.R., et al.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77–85 (2017)Google Scholar
  6. 6.
    Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587 (2014)Google Scholar
  7. 7.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)Google Scholar
  8. 8.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards teal-Time object detection with region proposal networks. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)Google Scholar
  9. 9.
    Lin, T., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117–2125 (2017)Google Scholar
  10. 10.
    Dai, J., et al.: R-fcn: object detection via region-based fully convolutional networks. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 379–387 (2016)Google Scholar
  11. 11.
    He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015). arXiv:1409.1556
  13. 13.
    Sermanet, P., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2014). arXiv:1312.6229
  14. 14.
    Gomez-Donoso, F., et al.: Lonchanet: a sliced-based cnn architecture for real-time 3d object recognition. In: 2017 International Joint Conference on Neural Networks (IJCNN) (2017), pp. 412–418Google Scholar
  15. 15.
    Ronneberger, O., Fischer, P. and Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Zhicong Yan
    • 1
  • Shuai Feng
    • 1
  • Fangqi Li
    • 1
  • Zhengwu Xu
    • 2
  • Shenghong Li
    • 1
    Email author
  1. 1.School of Cyber SecurityShanghai JiaoTong UniversityShanghaiChina
  2. 2.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations