An Introduction to Random Forests for Multi-class Object Detection

  • Juergen Gall
  • Nima Razavi
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)

Abstract

Object detection in large-scale real-world scenes requires efficient multi-class detection approaches. Random forests have been shown to handle large training datasets and many classes for object detection efficiently. The most prominent example is the commercial application of random forests for gaming [37]. In this paper, we describe the general framework of random forests for multi-class object detection in images and give an overview of recent developments and implementation details that are relevant for practitioners.

Keywords

multi-class object detection Hough forest regression forest random forest 

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References

  1. 1.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  2. 2.
    Barinova, O., Lempitsky, V., Kohli, P.: On the detection of multiple object instances using hough transforms. In: IEEE Conf. Computer Vision and Pattern Recognition (2010)Google Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Bosch, A., Zisserman, A., Muñoz, X.: Image classification using random forests and ferns. In: Int’l Conf. Computer Vision (2007)Google Scholar
  5. 5.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Chen, H.-T., Liu, T.-L., Fuh, C.-S.: Segmenting Highly Articulated Video Objects with Weak-Prior Random Forests. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 373–385. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Tech. Rep. MSR-TR-2011-114, Microsoft Research, Cambridge (2011)Google Scholar
  8. 8.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in ct studies. In: Medical Computer Vision Workshop (2010)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  10. 10.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: IEEE Conf. Computer Vision and Pattern Recognition (2009)Google Scholar
  11. 11.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Analysis and Machine Intelligence (2012)Google Scholar
  12. 12.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  13. 13.
    Fanelli, G., Gall, J., Van Gool, L.: Real time head pose estimation with random regression forests. In: IEEE Conf. Computer Vision and Pattern Recognition (2011)Google Scholar
  14. 14.
    Fanelli, G., Weise, T., Gall, J., Van Gool, L.: Real time head pose estimation from consumer depth cameras. In: Pattern Recognition, pp. 101–110 (2011)Google Scholar
  15. 15.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Analysis and Machine Intelligence 32, 1627–1645 (2010)CrossRefGoogle Scholar
  16. 16.
    Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: IEEE Conf. Computer Vision and Pattern Recognition (2009)Google Scholar
  17. 17.
    Gall, J., Razavi, N., Van Gool, L.: On-line adaption of class-specific codebooks for instance tracking. In: British Machine Vision Conf. (2010)Google Scholar
  18. 18.
    Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough forests for object detection, tracking, and action recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 33, 2188–2202 (2011)CrossRefGoogle Scholar
  19. 19.
    Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: Int’l Conf. Computer Vision (2011)Google Scholar
  20. 20.
    Godec, M., Roth, P., Bischof, H.: Hough-based tracking of non-rigid objects. In: Int’l Conf. Computer Vision (2011)Google Scholar
  21. 21.
    Lehmann, A., Leibe, B., Van Gool, L.: Fast prism: Branch and bound hough transform for object class detection. Int’l J. Computer Vision 94, 175–197 (2011)MATHCrossRefGoogle Scholar
  22. 22.
    Leibe, B., Cornelis, N., Cornelis, K., Van Gool, L.: Dynamic 3d scene analysis from a moving vehicle. In: IEEE Conf. Computer Vision and Pattern Recognition (2007)Google Scholar
  23. 23.
    Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. Int’l J. Computer Vision 77(1-3), 259–289 (2008)CrossRefGoogle Scholar
  24. 24.
    Leistner, C., Godec, M., Schulter, S., Saffari, A., Werlberger, M., Bischof, H.: Improving classifiers with unlabeled weakly-related videos. In: IEEE Conf. Computer Vision and Pattern Recognition (2011)Google Scholar
  25. 25.
    Leistner, C., Saffari, A., Santner, J., Bischof, H.: Semi-supervised random forests. In: Int’l Conf. Computer Vision, pp. 506–513 (2009)Google Scholar
  26. 26.
    Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 775–781 (2005)Google Scholar
  27. 27.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91 (2004)CrossRefGoogle Scholar
  28. 28.
    Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 34–40 (2005)Google Scholar
  29. 29.
    Menze, B.H., Kelm, B.M., Splitthoff, D.N., Koethe, U., Hamprecht, F.A.: On Oblique Random Forests. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS, vol. 6912, pp. 453–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  30. 30.
    Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: Neural Information Processing Systems (2006)Google Scholar
  31. 31.
    Okada, R.: Discriminative generalized hough transform for object dectection. In: Int’l Conf. Computer Vision (2009)Google Scholar
  32. 32.
    Razavi, N., Gall, J., Van Gool, L.: Backprojection Revisited: Scalable Multi-view Object Detection and Similarity Metrics for Detections. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 620–633. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Razavi, N., Gall, J., Van Gool, L.: Scalable multi-class object detection. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 1505–1512 (2011)Google Scholar
  34. 34.
    Rematas, K., Leibe, B.: Efficient object detection and segmentation with a cascaded hough forest ism. In: IEEE Workshop on Challenges and Opportunities in Robot Perception (2011)Google Scholar
  35. 35.
    Schroff, F., Criminisi, A., Zisserman, A.: Object class segmentation using random forests. In: British Machine Vision Conf. (2008)Google Scholar
  36. 36.
    Schulter, S., Leistner, C., Roth, P., Bischof, H., Van Gool, L.: On-line hough forests. In: British Machine Vision Conf. (2011)Google Scholar
  37. 37.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conf. Computer Vision and Pattern Recognition (2011)Google Scholar
  38. 38.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conf. Computer Vision and Pattern Recognition (2008)Google Scholar
  39. 39.
    Sun, M., Bradski, G., Xu, B.-X., Savarese, S.: Depth-Encoded Hough Voting for Joint Object Detection and Shape Recovery. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 658–671. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  40. 40.
    Winn, J., Shotton, J.: The layout consistent random field for recognizing and segmenting partially occluded objects. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 37–44 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juergen Gall
    • 1
    • 2
  • Nima Razavi
    • 1
  • Luc Van Gool
    • 1
    • 3
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.Max Planck Institute for Intelligent SystemsGermany
  3. 3.ESAT/IBBTKatholieke Universiteit LeuvenBelgium

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