Pedestrian Counting System Based on Multiple Object Detection and Tracking

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

With the increasing demands on video surveillance and business promotion, effective pedestrian counting in surveillance environments has become a hot research topic in computer vision. In this paper, we implement a pedestrian counting system based on multiple object detection and tracking. Region proposal network (RPN) and Real Adaboost classifier are employed to train a head-shoulder detector with high accuracy. We utilize the DSST algorithm to track the position transformations and the size changes of pedestrians. By combining human detection with object tracking together and using detection results to optimize the tracking algorithm, the pedestrian counting system is developed with high robustness against occlusions. We evaluated the system on the videos recorded in the subway station. The results showed that our system achieves a high accuracy and can be used for pedestrian counting in crowded public places.

Keywords

Pedestrian counting Human detection Object tracking 

Notes

Acknowledgement

The work was supported by the National Natural Science Foundation of China (Grant No. 91420302), the National Basic Research Program of China (Grant No. 2015CB856004) and the Key Basic Research Program of Shanghai, China (15JC1400103).

References

  1. 1.
    Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 613–627. Springer, Cham (2015). doi: 10.1007/978-3-319-16181-5_47 Google Scholar
  2. 2.
    Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC 2009. BMVA Press (2009)Google Scholar
  3. 3.
    Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE PAMI 36(8), 1532–1545 (2014)CrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005. vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  5. 5.
    Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: NIPS 2014, pp. 424–432. MIT Press (2014)Google Scholar
  6. 6.
    Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: CVPR 2015, pp. 1751–1760. IEEE (2015)Google Scholar
  7. 7.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE PAMI 39(6), 1137–1149 (2017)CrossRefGoogle Scholar
  8. 8.
    Girshick, R.: Fast R-CNN. In: ICCV 2015, pp. 1440–1448. IEEE Computer Society (2015)Google Scholar
  9. 9.
    Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 443–457. Springer, Cham (2016). doi: 10.1007/978-3-319-46475-6_28 CrossRefGoogle Scholar
  10. 10.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR 2000, vol. 2, pp. 142–149. IEEE (2000)Google Scholar
  11. 11.
    Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: CVPR 2015, pp. 3119–3127. IEEE Computer Society (2015)Google Scholar
  12. 12.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR 2010, pp. 2544–2550. IEEE (2010)Google Scholar
  13. 13.
    Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC 2014. BMVA Press (2014)Google Scholar
  14. 14.
  15. 15.
    Zhang, X., Zhang, L.: Real time crowd counting with human detection and human tracking. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8836, pp. 1–8. Springer, Cham (2014). doi: 10.1007/978-3-319-12643-2_1 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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