Skip to main content
SpringerLink
Log in

Hough Forests Revisited: An Approach to Multiple Instance Tracking from Multiple Cameras

  • Conference paper
  • First Online:
Pattern Recognition (GCPR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8753))

Included in the following conference series:

  • 2769 Accesses

Abstract

Tracking multiple objects in parallel is a difficult task, especially if instances are interacting and occluding each other. To alleviate the arising problems multiple camera views can be taken into account, which, however, increases the computational effort. Evoking the need for very efficient methods, often rather simple approaches such as background subtraction are applied, which tend to fail for more difficult scenarios. Thus, in this work, we introduce a powerful multi-instance tracking approach building on Hough Forests. By adequately refining the time consuming building blocks, we can drastically reduce their computational complexity without a significant loss in accuracy. In fact, we show that the test time can be reduced by one to two orders of magnitude, allowing to efficiently process the large amount of image data coming from multiple cameras. Furthermore, we adapt the pre-trained generic forest model in an online manner to train an instance-specific model, making it well suited for multi-instance tracking. Our experimental evaluations show the effectiveness of the proposed efficient Hough Forests for object detection as well as for the actual task of multi-camera tracking.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that \(y = 0\) for the background class.

  2. 2.

    http://www.d2.mpi-inf.mpg.de/andriluka_cvpr08

  3. 3.

    http://pascal.inrialpes.fr/data/human/

References

  1. Amit, Y., Geman, D.: Randomized inquiries about shape; an application to handwritten digit recognition. Technical report 401, Department of Statistics, University of Chicago, IL (1994)

    Google Scholar 

  2. Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of CVPR (2008)

    Google Scholar 

  3. Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognit. 13(2), 111–122 (1981)

    Article  MATH  Google Scholar 

  4. Barinova, O., Lempitsky, V.S., Kohli, P.: On detection of multiple object instances using hough transforms. IEEE Trans. PAMI 34(9), 1773–1784 (2012)

    Article  Google Scholar 

  5. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36(1–2), 105–139 (1999)

    Article  Google Scholar 

  6. Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: Proceedings of CVPR (2012)

    Google Scholar 

  7. Berclaz, J., Fleuret, F., Fua, P.: Multiple object tracking using k-shortest path optimization. IEEE Trans. PAMI 9(33), 1806–1819 (2011)

    Article  Google Scholar 

  8. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  9. Butt, A.A., Collins, R.T.: Multi-target tracking by lagrangian relaxation to min-cost network flow. In: Proceedings of CVPR (2013)

    Google Scholar 

  10. Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and accurate shape model fitting using random forest regression voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 278–291. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 7(2–3), 81–227 (2012)

    Google Scholar 

  12. Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time facial feature detection using conditional regression forests. In: Proc. CVPR (2012).

    Google Scholar 

  13. Dicle, C., Camps, O., Sznaier, M.: The way they move: tracking multiple targets with similar appearance. In: Proceedings of ICCV (2013)

    Google Scholar 

  14. Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: Proceedings of BMVC (2010)

    Google Scholar 

  15. Eshel, R., Moses, Y.: Tracking in a dense crowd using multiple cameras. IJCV 88(1), 129–143 (2010)

    Article  Google Scholar 

  16. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)

    Article  Google Scholar 

  17. Gall, J., Razavi, N., Van Gool, L.: On-line adaption of class-specific codebooks for instance tracking. In: Proceedings of BMVC (2010)

    Google Scholar 

  18. Gall, J., Razavi, N., Van Gool, L.: An introduction to random forests for multi-class object detection. In: Dellaert, F., Frahm, J.-M., Pollefeys, M., Leal-Taixé, L., Rosenhahn, B. (eds.) Real-World Scene Analysis 2011. LNCS, vol. 7474, pp. 243–263. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.S.: Hough forests for object detection, tracking, and action recognition. IEEE Trans. PAMI 33(11), 2188–2202 (2011)

    Article  Google Scholar 

  20. Girshick, R.B., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.W.: Efficient regression of general-activity human poses from depth images. In: Proceedings of ICCV (2011)

    Google Scholar 

  21. Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. Comput. Vis. Image Underst. 117(10), 1245–1256 (2013)

    Article  Google Scholar 

  22. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. PAMI 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  23. Khan, S.M., Shah, M.: Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. PAMI 31(3), 505–519 (2009)

    Article  Google Scholar 

  24. Küttel, D., Breitenstein, M.D., Van Gool, L., Ferrari, V.: What’s going on? discovering spatio-temporal dependencies in dynamic scenes. In: Proceedings of CVPR (2010)

    Google Scholar 

  25. Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77(1–3), 259–289 (2008)

    Article  Google Scholar 

  26. Liu, J., Carr, P., Collins, R.T., Liu, Y.: Tracking sports players with context-conditioned motion models. In: Proceedings of CVPR (2013)

    Google Scholar 

  27. Okada, R.: Discriminative generalized hough transform for object dectection. In: Proceedings of ICCV (2009)

    Google Scholar 

  28. Özuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. PAMI 32(3), 448–461 (2010)

    Article  Google Scholar 

  29. Perlich, C., Provost, F.J., Simonoff, J.S.: Tree induction vs. logistic regression: a learning-curve analysis. J. Mach. Learn. Res. 4, 211–255 (2003)

    MathSciNet  Google Scholar 

  30. Possegger, H., Sternig, S., Mauthner, T., Roth, P.M., Bischof, H.: Robust real-time tracking of multiple objects by volumetric mass densities. In: Proceedings of CVPR (2013)

    Google Scholar 

  31. Roth, P.M., Leistner, C., Berger, A., Bischof, H.: Multiple instance learning from multiple cameras. In: IEEE Workshop on Camera Networks (CVPR) (2010)

    Google Scholar 

  32. Schreiber, D., Cambrini, L., Biber, J., Sardy, B.: Online visual quality inspection for weld seams. Int. J. Adv. Manuf. Technol. 42(5–6), 497–504 (2008)

    Google Scholar 

  33. Schulter, S., Leistner, C., Roth, P.M., Van Gool, L., Bischof, H.: On-line hough forests. In: Proceedings of BMVC (2011)

    Google Scholar 

  34. Shotton, J., Girshick, R.B., Fitzgibbon, A.W., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Trans. PAMI 35(12), 2821–2840 (2013)

    Article  Google Scholar 

  35. Sternig, S., Mauthner, T., Irschara, A., Roth, P.M., Bischof, H.: Multi-camera multi-object tracking by robust hough-based homography projections. In: IEEE Workshop on Visual Surveillance (ICCV) (2011)

    Google Scholar 

  36. Tang, D., Liu, Y., Kim, T.K.: Fast pedestrian detection by cascaded random forest with dominant orientation templates. In: Proceedings of BMVC (2012)

    Google Scholar 

  37. Tang, D., Yu, T.H., Kim, T.K.: Real-time articulated hand pose estimation using semi-supervised transductive regression forests. In: Proceedings of ICCV (2013)

    Google Scholar 

  38. Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.: Learning people detectors for tracking in crowded scenes. In: Proceedings of ICCV (2013)

    Google Scholar 

  39. Wohlhart, P., Donoser, M., Roth, P.M., Bischof, H.: Detecting partially occluded objects with an implicit shape model random field. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 302–315. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  40. Roshan Zamir, A., Dehghan, A., Shah, M.: GMCP-tracker: global multi-object tracking using generalized minimum clique graphs. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 343–356. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

Download references

Acknowledgment

This work was supported by the Austrian Science Foundation (FWF) project Advanced Learning for Tracking and Detection in Medical Workflow Analysis (I535-N23).

Author information

Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Georg Poier, Samuel Schulter, Sabine Sternig, Peter M. Roth & Horst Bischof

Authors
  1. Georg Poier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel Schulter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabine Sternig
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter M. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Horst Bischof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georg Poier .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Münster, Germany

    Xiaoyi Jiang

  2. Computer Science Department 5, University of Erlangen-Nürnberg, Erlangen, Germany

    Joachim Hornegger

  3. Department of Computer Science, University of Kiel, Kiel, Germany

    Reinhard Koch

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Poier, G., Schulter, S., Sternig, S., Roth, P.M., Bischof, H. (2014). Hough Forests Revisited: An Approach to Multiple Instance Tracking from Multiple Cameras. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11752-2_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11751-5

  • Online ISBN: 978-3-319-11752-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation