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Unique people count from monocular videos

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Abstract

Counting unique number of people in a video (i.e., counting a person only once while the person passes through the field of view) is required in many video analytic applications, such as transit passenger and pedestrian volume count in railway stations, malls, and road intersections. The principal roadblock here is occlusion. To avoid this bottleneck, we adopt a combination of (a) a radical new approach of unique influx and outflux count (UIOC) of people within a region of interest (ROI), which is adopted from computational fluidics, (b) a nonlinear regressor to estimate the number of people within a ROI, and (c) ROI boundary tracking (as opposed to object or feature tracking) for a short period. In UIOC, we compute influx/outflux rate, i.e., number of people entering or exiting the ROI per unit time. Then, we sum the influx/outflux rate between any two time points to estimate the number of people that entered and/or left the ROI within that time interval. Our framework is validated on 19 publicly available datasets, with abundant occlusion, obtaining more than 95 % accuracy for each video. Our framework is online and real time. Our framework is comparatively inexpensive to install and operate as only one camera is used. These features make the proposed framework suitable for low-cost, small-business/residential and/or commercial applications. We also extend our framework beyond monocular videos and apply it on multiple views of a publicly available dataset with about 99 % accuracy.

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References

  1. Box, P.C., Oppenlander, J.C.: Manual of traffic engineering studies. Institute of Transportation Engineers, pp. 17 (2010)

  2. Chan, A.B., Liang, Z.-S.J., Vasconcelos, N.: Privacy preserving crowd montioring: counting people without people models or tracking. In: CVPR, vol. 1, pp. 1–7 (2008)

  3. Chan, A.B., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. Trans. Image Process. 21, 2160–2177 (2012)

    Article  MathSciNet  Google Scholar 

  4. Chan, A.B., Vasconcelos, N.: Bayesian Poisson regression for crowd counting. In: ICCV, vol. 1, pp. 17 (2009)

  5. Conte, D., Foggia, P., Percannella, G., Vento, M.: Counting moving persons in crowded scenes. Mach. Vis. Appl. 24, 1029–1042 (2013)

    Article  Google Scholar 

  6. Conte, D., Foggia, P., Percannella, G., Tufano, F., Vento, M.: Counting moving people in videos by salient points detection. In: ICPR, vol. 1, pp. 1743–1746 (2010)

  7. Tan, B., Zhang, J., Wang, L.: Semi-supervised elastic net for pedestrian counting. Pattern Recognit. 44, 2297–2304 (2011)

    Article  Google Scholar 

  8. Lempitsky, V., Zisserman, A.: Learning to count objects in images. NIPS. 23, 1324–1332 (2010)

    Google Scholar 

  9. Harasse, S., Bonnaud, L., Desvignes, M.: People counting in transport vehicles. Trans. Eng. Comput. Technol. 4, 221–224 (2005)

    Google Scholar 

  10. Kim, J.W., Choi, K.S., Choi, B.D., Ko, S.J.: Real-time vision-based people counting system for the security door. In: ITC-CSCC, vol. 1, pp. 1418–1421 (2002)

  11. Zeng, C., Ma, H.: Robust head-shoulder detection by PCA-based multilevel HOG-LBP detector for people counting. ICPR. 1, 2069–2072 (2010)

    Google Scholar 

  12. Brostow, G.J., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: CVPR, vol. 1, pp. 594–601 (2006)

  13. Rabaud, V., Belongie, S.J.: Counting crowded moving objects. In: CVPR, vol. 1, pp. 705–711 (2006)

  14. Ma, Z., Chan, A.B.: Crossing the line: crowd counting by integer programming with local features. In: CVPR, vol. 1, pp. 2539–2576 (2013)

  15. Cong, Y., Gong, H., Zhu, S., Tang, Y.: Flow mosaicking: real-time pedestrian counting without scene-specific learning. In: CVPR, vol. 1, pp. 1093–1100 (2009)

  16. Kim, Y.-S., Lee1, G.-G., Yoon, J.-Y., Kim, J.-J., Kim, W.-Y.: A method of counting pedestrians in crowded scenes. In: ICIC, vol. 1, pp. 1117–1126 (2008)

  17. Mukherjee, S., Saha, B., Jamal, I., Leclerc, R., Ray, N.: A novel framework for automatic passenger counting. In: ICIP, vol. 1, pp. 2969–2972 (2011)

  18. Yao, B., Yang, X., Zhu, S.: Introduction to a large-scale general purpose ground truth database: methodology, annotation tool and benchmarks. In EMM-CVPR. 1679, 169–183 (2007) http://www.cvg.rdg.ac.uk/PETS2013/a.html#s1

  19. Mukherjee, S., Ray, N., Acton, S.: Counting cells from microscopy videos without tracking individual cells. In: IEEE ISBI (2014 accepted)

  20. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, vol. 1, pp. 246–252 (1999)

  21. Barnich, O., Droogenbroeck, M.V.: ViBe: a universal background subtraction algorithmfor video sequences. Trans. Image Process. 20, 1709–1724 (2011)

    Article  Google Scholar 

  22. McFarlane, N.J.B., Schofield, C.P.: Segmentation and tracking of piglets in images. Mach. Vis. Appl. 8, 187–193 (1995)

    Article  Google Scholar 

  23. Hou, C.P., Zhang, C.S., Wu, Y., Nie, F.P.: Multiple view semi-supervised dimensionality reduction. Pattern Recognit. 43, 720–730 (2010)

    Article  MATH  Google Scholar 

  24. Zhu, X.J.: Semi-supervised learning literature survey. Technical Report 1530. Computer Sciences, University of Wisconsin-Madison, USA (2005).

  25. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  26. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)

    Article  Google Scholar 

  27. Kharbat, M.: Horn–Schunck optical flow method. http://www.mathworks.com/matlabcentral/fileexchange/22756-horn-schunck-optical-flow-method. Accessed 14 Nov 2011

  28. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)

  29. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray scale and rotation invariant texture analysis with local binary patterns. Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)

    Article  Google Scholar 

  30. Chateau, T., Gay-Belille, V., Chausse, F., Lapreste, J.: Real-time tracking with classifiers. In: ECCV, vol. 1, pp. 218–231 (2006)

  31. Krahnstoever, N., Yu, T., Patwardhan, K.A., Gao, D.: Multi-camera person tracking in crowded environments. In: PETS-Winter Workshop, vol. 1, pp. 1–7 (2008)

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Acknowledgments

The authors would like to thank Dr. Yang Cong for the LHI dataset. The authors also acknowledge the following sources of funding for this work: NSERC, AQL Management Consulting Inc., and Computing Science, University of Alberta.

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Authors and Affiliations

  1. Department of Computing Sciences, University of Alberta, Edmonton, Canada

    Satarupa Mukherjee, Stephani Gil & Nilanjan Ray

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  1. Satarupa Mukherjee
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  2. Stephani Gil
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  3. Nilanjan Ray
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Correspondence to Satarupa Mukherjee.

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Mukherjee, S., Gil, S. & Ray, N. Unique people count from monocular videos. Vis Comput 31, 1405–1417 (2015). https://doi.org/10.1007/s00371-014-1022-6

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