Skip to main content
SpringerLink
Log in

People-flow counting in complex environments by combining depth and color information

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

People-flow counting is one of the key techniques of intelligence video surveillance systems and the information of people-flow obtained from this technique is an very important evidence for many applications, such as business analysis, staff planning, security, etc. Traditionally, the color image information based methods encounter kinds of challenges, such as shadows, illumination changing, cloth color, etc., while the depth information based methods suffer from lack of texture. In this paper, we propose an effective approach of people-flow counting by combining color and depth information. First, we adopt a background subtraction technique to fast obtain the moving regions on depth images. Second, the water filling algorithm is used to effectively detect head candidates on the moving regions. Then we use the SVM to recognize the real heads from the candidates. Finally, we adopt a weighted K Nearest Neighbor based multi-target tracking method to track each confirmed head and count the people through the surveillance region. Four datasets constructed from two surveillance scenes are used to evaluate the proposed method. Experimental results show that our method outperform the state-of-the-art methods. Our method can work stably on condition of kinds of interruptions and can not only obtain high precisions, but also high recalls on four datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Benfold B, Reid I (2011) Stable multi-target tracking in real-time surveillance video. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3457–3464

  2. Cai Z, Yu ZL, Liu H, Zhang K (2014) Counting people in crowded scenes by video analyzing. In: 2014 IEEE 9th conference on industrial electronics and applications (ICIEA). IEEE, pp 1841– 1845

  3. Chen TH, Chen TY, Chen ZX (2006) An intelligent people-flow counting method for passing through a gate. In: 2006 IEEE conference on robotics, automation and mechatronics. IEEE, pp 1–6

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, vol 1. IEEE, pp 886– 893

  5. Dan BK, Kim YS, Jung JY, Ko SJ, et al. (2012) Robust people counting system based on sensor fusion. In: IEEE transactions on consumer electronics, vol 58, pp 1013–1021

  6. Evangelio RH, Patzold M, Keller I, Sikora T (2014) Adaptively splitted gmm with feedback improvement for the task of background subtraction. In: IEEE transactions on information forensics and security, vol 9, pp 863–874

  7. Fu H, Ma H, Xiao H (2014) Scene-adaptive accurate and fast vertical crowd counting via joint using depth and color information. Multimedia Tools and Applications 73(1):273–289

    Article  Google Scholar 

  8. Galčík F, Gargalík R. (2013) Real-time depth map based people counting. In: Advanced concepts for intelligent vision systems. Springer, pp 330–341

  9. Hernandez D, Castrillon M, Lorenzo J (2011) People counting with re-identification using depth cameras. IET Conference Proceedings pp 16–16(1)

  10. Hsieh CT, Wang HC, Wu YK, Chang LC, Kuo TK (2012) A kinect-based people-flow counting system. In: 2012 international symposium on intelligent signal processing and communications systems (ISPACS). IEEE, pp 146–150

  11. Hu Y, Zhou P, Zhou H (2011) A new fast and robust method based on head detection for people-flow counting system. International Journal of Information Engineering 1(1):33–43

    Google Scholar 

  12. Li B, Zhang J, Zhang Z, Xu Y (2014) A people counting method based on head detection and tracking. In: 2014 international conference on smart computing (SMARTCOMP). IEEE, pp 136–141

  13. Lin Y, Liu N (2012) Integrating bottom-up and top-down processes for accurate pedestrian counting. In: 2012 21st international conference on pattern recognition (ICPR). IEEE, pp 2508–2511

  14. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. pp 1617–1623

  15. Mukherjee S, Das K (2013) An adaptive gmm approach to background subtraction for application in real time surveillance. arXiv:1307.5800

  16. Nie L, Wang M, Zha ZJ, Chua TS (2012) Oracle in image search: a content-based approach to performance prediction. ACM Trans Inf Syst (TOIS) 30 (2):13

    Article  Google Scholar 

  17. Nie L, Yan S, Wang M, Hong R, Chua TS (2012) Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM international conference on multimedia. ACM, pp 59–68

  18. Raheja J, Kalita S, Dutta PJ, Lovendra S (2012) A robust real time people tracking and counting incorporating shadow detection and removal. Int J Comput Appl 46(4):51–58

    Google Scholar 

  19. Rauter M (2013) Reliable human detection and tracking in top-view depth images. In: 2013 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 529–534

  20. Stahlschmidt C, Gavriilidis A, Kummert A (2013) Density measurements from a top-view position using a time-of-flight camera. In: Proceedings of the 8th international workshop on Multidimensional systems (nDS), 2013. VDE, pp 1–6

  21. Suresh S, Deepak P, Chitra K (2014) An efficient low cost background subtraction method to extract foreground object during human tracking. In: 2014 international conference on circuit, power and computing technologies (ICCPCT). IEEE, pp 1432–1436

  22. Tan S (2005) Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Syst Appl 28(4):667–671

    Article  Google Scholar 

  23. Tong S, Koller D (2002) Support vector machine active learning with applications to text classification. J Mach Learn Res 2:45–66

    MATH  Google Scholar 

  24. Van Oosterhout T, Bakkes S, Kröse BJ (2011) Head detection in stereo data for people counting and segmentation. In: VISAPP, pp 620–625

  25. Wateosot C, Suvonvorn N (2013) Top-view based people counting using mixture of depth and color information. In: The second asian conference on information systems, ACIS

  26. Yam KY, Siu WC, Law NF, Chan CK (2011) Effective bi-directional people flow counting for real time surveillance system. In: Proceedings, ICCE, vol 11, pp 863–864

  27. Yan Y, Ricci E, Subramanian R, Lanz O, Sebe N (2013) No matter where you are: flexible graph-guided multi-task learning for multi-view head pose classification under target motion. In: 2013 IEEE international conference on computer vision (ICCV). IEEE, pp 1177–1184

  28. Yan Y, Ricci E, Subramanian R, Liu G, Lanz O, Sebe N (2015) A multi-task learning framework for head pose estimation under target motion. IEEE Trans Pattern Anal Mach Intell PP(99):1–1. doi:10.1109/TPAMI.2015.2477843

    Article  Google Scholar 

  29. Yan Y, Shen H, Liu G, Ma Z, Gao C, Sebe N (2014) Glocal tells you more: coupling glocal structural for feature selection with sparsity for image and video classification. Comput Vis Image Underst 124:99–109

    Article  Google Scholar 

  30. Zeng C, Ma H (2010) Robust head-shoulder detection by pca-based multilevel hog-lbp detector for people counting. In: 2010 20th international conference on pattern recognition (ICPR). IEEE, pp 2069–2072

  31. Zhang X, Yan J, Feng S, Lei Z, Yi D, Li SZ (2012) Water filling: unsupervised people counting via vertical kinect sensor 2012 IEEE 9th international conference on advanced video and signal-based surveillance (AVSS). IEEE, pp 215–220

  32. Zhao YL, Nie L, Wang X, Chua TS (2014) Personalized recommendations of locally interesting venues to tourists via cross-region community matching. ACM Trans Intell Syst Technol (TIST) 5(3):50

    Google Scholar 

  33. Zhu L, Wong KH (2013) Human tracking and counting using the kinect range sensor based on adaboost and kalman filter. In: Advances in visual computing. Springer, pp 582–591

  34. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition, ICPR 2004, vol 2. IEEE, pp 28–31

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61571071, 61102131), the Natural Science Foundation of Chongqing Science and Technology Commission (No. cstc2014jcyjA40048), Wenfeng innovation and start-up project of Chongqing University of Posts and Telecommunications (No. WF201404).

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing, China

    Chenqiang Gao, Jun Liu, Qi Feng & Jing Lv

Authors
  1. Chenqiang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chenqiang Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, C., Liu, J., Feng, Q. et al. People-flow counting in complex environments by combining depth and color information. Multimed Tools Appl 75, 9315–9331 (2016). https://doi.org/10.1007/s11042-016-3344-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3344-z

Keywords

Search

Navigation