Advertisement

Robust Pedestrian Detection Based on Parallel Channel Cascade Network

  • Jiaojiao HeEmail author
  • Yongping Zhang
  • Tuozhong Yao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Promoted by Smart City, pedestrian detection under wide-angle surveillance has attracted much attention. Aiming to the small-size pedestrians have poor resolution and different degrees of distortion in visual picture from wide-angle field of view, a robust pedestrian detection algorithm based on parallel channel cascade network is proposed. The algorithm, an improved Faster R-CNN (Faster Region Convolutional Neural Networks), first obtains the differential graph and original graph to construct parallel input, and then introduces a new feature extraction network, which called the Channel Cascade Network (CCN), further designs parallel CCN for fusing more abundant image features. Finally, in Region Proposal Network, the size distribution of pedestrians in the picture is counted by clustering to best fit the pedestrian date sets. Compared with the standard Faster-RCNN and the FPN, the proposed algorithm is more conducive to the small-size pedestrian detection in the case of wide angle field distortion.

Keywords

Parallel cascading channel network Small-size pedestrian detection Wide-angle surveillance Regional proposal Clustering 

Notes

Acknowledgments

This work is supported in part by National Natural Science Foundation of China (N0. 61771270), in part by Natural Science Foundation of Zhejiang Province (No. LY9F0001, No. LQ15F020004, No. LY19F010006), and by Key research and development plan of Zhejiang province (2018C01086).

References

  1. 1.
    Zhang, Q.: Research on pedestrian detection methods on still images. University of Science and Technology of China (2015). (in Chinese)Google Scholar
  2. 2.
    Lin, T.Y., Dollar, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 936–944 (2016)Google Scholar
  3. 3.
    Wang, B.: Pedestrian detection based on deep learning. Beijing Jiaotong University (2015). (in Chinese)Google Scholar
  4. 4.
    Zhang, J., Xiao, J., Zhou, C., et.al: A multi-class pedestrian detection network for distorted pedestrians. In: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), May, pp. 1079–1083 (2018)Google Scholar
  5. 5.
    He, J., Liu, K., Zhang, Y., et al: A channel-cascading pedestrian detection network for small-size pedestrians, pp. 325–338. Springer (2018)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004). IEEE Computer Society Conference on Computer Vision and Pattern RecognitionCrossRefGoogle Scholar
  7. 7.
    Dalal, N., Triggs, B., et al.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  8. 8.
    Zhu, Q., Yeh, M.C., Cheng, K.T., et al.: Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1491–1498 (2006)Google Scholar
  9. 9.
    Burges, C.J.: A tutorial on support vector machines for pattern recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 121–167 (1998)Google Scholar
  10. 10.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37. Springer, Berlin (1995)Google Scholar
  11. 11.
    Felzenszwalb, P.F., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRefGoogle Scholar
  12. 12.
    Wang, X.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan, September, pp. 32–39 (2009)Google Scholar
  13. 13.
    Kuo, W., Hariharan, B., Malik, J..: DeepBox: learning objectness with convolutional networks. In IEEE International Conference on Computer Vision, pp. 2479–2487 (2015)Google Scholar
  14. 14.
    Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1904–1916 (2014)Google Scholar
  15. 15.
    He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904–1916 (2014)CrossRefGoogle Scholar
  16. 16.
    Girshick, R.: Fast R-CNN. In: Computer Science, pp. 1440–1448 (2015)Google Scholar
  17. 17.
    Ren, S., Girshick, R., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems, pp. 1137–1149 (2015)CrossRefGoogle Scholar
  18. 18.
    Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger, pp. 6517–6525 (2016)Google Scholar
  19. 19.
    Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2018). arXiv:1804.02767
  20. 20.
    Liu, W., Anguelov, D., Erhan, D., et al.: SSD: single shot MultiBox detector. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 21–37 (2015)CrossRefGoogle Scholar
  21. 21.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)Google Scholar
  22. 22.
    Kong, T., Yao, A., Chen, Y., et al: HyperNet: towards accurate region proposal generation and joint object detection. In: Computer Vision and Pattern Recognition, pp. 845–853 (2016)Google Scholar
  23. 23.
    Cai, Z., et al.: A unified multi-scale deep convolutional neural network for fast object detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 354–370 (2016)CrossRefGoogle Scholar
  24. 24.
    Hariharan, B., Arbelaez, P., Girshick, R., et al: Hypercolumns for object segmentation and fine-grained localization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 447–456 (2014)Google Scholar
  25. 25.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2014)Google Scholar
  26. 26.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computer Science, pp. 730–734 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringNingbo University of TechnologyNingboChina
  2. 2.School of Electronics and Control EngineeringChang’an UniversityXi’anChina

Personalised recommendations