Skip to main content
Springer Nature Link
Log in

SCOOP: A Real-Time Sparsity Driven People Localization Algorithm

  • Published:
Journal of Mathematical Imaging and Vision Aims and scope Submit manuscript

Abstract

Detecting and tracking people in scenes monitored by cameras is an important step in many application scenarios such as surveillance, urban planning or behavioral studies to name a few. The amount of data produced by camera feeds is so large that it is also vital that these steps be performed with the utmost computational efficiency and often even real-time. We propose SCOOP, a novel algorithm that reliably localizes people in camera feeds, using only the output of a simple background removal technique. SCOOP can handle a single or many video feeds. At the heart of our technique there is a sparse model for binary motion detection maps that we solve with a novel greedy algorithm based on set covering. We study the convergence and performance of the algorithm under various degradation models such as noisy observations and crowded environments, and we provide mathematical and experimental evidence of both its efficiency and robustness using standard datasets. This clearly shows that SCOOP is a viable alternative to existing state-of-the-art people localization algorithms, with the marked advantage of real-time computations.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. Dataset is publicly available at http://www.apidis.org/Dataset/.

  2. Dataset is publicly available at http://winterpets09.net/.

  3. Here, unless otherwise specified, SCOOP applies the third stopping criteria.

  4. Note that, |.| denotes the absolute value of a scalar, whereas for a set (and as previously defined) it returns its cardinality.

References

  1. Alahi, A., Jacques, L., Boursier, Y., Vandergheynst, P.: Sparsity driven people localization with a heterogeneous network of cameras. J. Math. Imaging Vis. (2011). doi:10.1007/s10851-010-0258-7

    MATH  MathSciNet  Google Scholar 

  2. Berclaz, J., Fleuret, F., Fua, P.: Robust people tracking with global trajectory optimization. In: Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  3. Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: Proc. IEEE Workshop on Motion and Video Computing, p. 169 (2002)

    Google Scholar 

  4. Blumensath, T., Davies, M.: Iterative thresholding for sparse approximations. J. Fourier Anal. Appl. 14(5), 629–654 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  5. Candès, E.J., Wakin, M., Boyd, S.: Enhancing sparsity by reweighted ℓ 1 minimization. J. Fourier Anal. Appl. 14(5), 877–905 (2008). Special issue on sparsity

    Article  MATH  MathSciNet  Google Scholar 

  6. Caspi, Y., Simakov, D., Irani, M.: Feature-based sequence-to-sequence matching. Int. J. Comput. Vis. 68(1), 53–64 (2006)

    Article  Google Scholar 

  7. Cheraghchi, M., Hormati, A., Karbasi, A., Vetterli, M.: Group testing with probabilistic tests: Theory, design and application. IEEE Trans. Inf. Theory (2010)

  8. Delannay, D., Danhier, N., De Vleeschouwer, C.: Detection and recognition of sports(wo)man from multiple views. In: Proc. ACM/IEEE Int’l Conference on Distributed Smart Cameras, Como, Italy (2009)

    Google Scholar 

  9. Dorfman, R.: The detection of defective members of large populations. Ann. Math. Stat. 14, 436–440 (1943)

    Article  Google Scholar 

  10. Du, D.Z., Hwang, F.: Combinatorial Group Testing and Its Applications. World Scientific Series on Applied Mathematics (1999)

    Google Scholar 

  11. Ellis, A., Shahrokni, A., Ferryman, J.: Overall evaluation of the pets2009 results. In: Proc. IEEE Int’l Workshop on Performance Evaluation of Tracking and Surveillance. Snowbird, Utah (2009)

    Google Scholar 

  12. Eshel, R., Moses, Y.: Homography based multiple camera detection and tracking of people in a dense crowd. In: Proc. IEEE Int’l Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  13. Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)

    Article  Google Scholar 

  14. Fornasier, M., Rauhut, H.: Iterative thresholding algorithms. Appl. Comput. Harmon. Anal. 25(2), 187–208 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  15. Golbabaee, M., Vandergheynst, P.: Average case analysis of sparse recovery with Thresholding: New bounds based on average dictionary coherence. In: IEEE Int. Conf. on Acoustics, Speech & Signal Processing (ICASSP) (2008)

    Google Scholar 

  16. Kannala, J., Brandt, S.: A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1335 (2006)

    Article  Google Scholar 

  17. Khan, S., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Proc. European Conference on Computer Vision, pp. 133–146 (2006)

    Google Scholar 

  18. Khan, S.M., Mubarak, S.: Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 505–519 (2009)

    Article  Google Scholar 

  19. Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  20. Mueller, K., Smolic, A., Droese, M., Voigt, P., Wienand, T.: Multi-texture modeling of 3d traffic scenes. In: International Conference on Multimedia and Expo ICME’03. Proceedings. vol. 1, pp. 657–660 (2003). 2003. doi:10.1109/ICME.2003.1221003

    Google Scholar 

  21. Orwell, J., Massey, S., Remagnino, P., Greenhill, D., Jones, G.A.: A multi-agent framework for visual surveillance. In: Proc. IEEE Int’l Conference on Image Analysis and Processing, p. 1104. IEEE Computer Society, Washington, DC (1999)

    Chapter  Google Scholar 

  22. Porikli, F.: Achieving real-time object detection and tracking under extreme conditions. J. Real-Time Image Process. 1(1), 33–40 (2006)

    Article  Google Scholar 

  23. Schnass, K., Vandergheynst, P.: Average performance analysis for thresholding. IEEE Signal Process. Lett. 14(11), 828–831 (2007)

    Article  Google Scholar 

  24. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Int’l Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)

    Google Scholar 

  25. Stauffer, C., Tieu, K.: Automated multi-camera planar tracking correspondence modeling. In: Proc. IEEE Int’l Conference on Computer Vision and Pattern Recognition, pp. 259–266 (2003)

    Google Scholar 

  26. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58, 267–288 (1994)

    MathSciNet  Google Scholar 

  27. Vazirani, V.: Approximation Algorithms. Springer, Berlin (2001)

    Google Scholar 

Download references

Acknowledgements

Part of this work was funded by the EU under research projects FET-OPEN 225913 (SMALL), ICT-216023 (APIDIS), and by the Swiss National Science Foundation under grant 200021-117884.

Author information

Authors and Affiliations

  1. Signa Processing Lab (LTS2), EPFL, Station 11, 1015, Lausanne, Switzerland

    Mohammad Golbabaee, Alexandre Alahi & Pierre Vandergheynst

Authors
  1. Mohammad Golbabaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexandre Alahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Vandergheynst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Golbabaee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Golbabaee, M., Alahi, A. & Vandergheynst, P. SCOOP: A Real-Time Sparsity Driven People Localization Algorithm. J Math Imaging Vis 48, 160–175 (2014). https://doi.org/10.1007/s10851-012-0405-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10851-012-0405-4

Keywords

Search

Navigation