Skip to main content
SpringerLink
Log in

Object Detection

  • Chapter
  • First Online:
Ensemble Machine Learning

  • 13k Accesses

Abstract

Over the past twenty years, data-driven methods have become a dominant paradigm for computer vision, with numerous practical successes. In difficult computer vision tasks, such as the detection of object categories (for example, the detection of faces of various gender, age, race, and pose, under various illumination and background conditions), researchers generally learn a classifier that can distinguish an image patch that contains the object of interest from all other image patches. Ensemble learning methods have been very successful in learning classifiers for object detection.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover 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.

    After training the AdaBoost classifier (i.e., a node classifier in the cascade), one can adjust the threshold θ to meet the learning goal of a node classifier (e.g., a fixed detection rate or a fixed false positive rate.)

  2. 2.

    Special care is required for computing \({\Sigma }_{{\mathbf{x}}^{{\prime}}}\) efficiently. However, we omit these details. The readers may refer to Sect. 3.2 of [28] for more information.

  3. 3.

    In addition to the level 4 nodes shown in Fig. 8.4, Huang et al. rotate their features (called Granule features) by 90 ∘ , 180 ∘ , and − 90 ∘  for the level 4 nodes. This strategy effectively covers the entire 360 ∘  range in-plane rotation.

References

  1. Amit, Y., Geman, D., Fan, X.: A coarse-to-fine strategy for multiclass shape detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(12), 1606–1621 (2004)

    Article  Google Scholar 

  2. Avidan, S.: Ensemble tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)

    Article  Google Scholar 

  3. Avidan, S., Butman, M.: The power of feature clustering: An application to object detection. In: Advances in Neural Information Processing Systems 17, pp. 57–64 (2005)

    Google Scholar 

  4. Baker, S., Nayar, S.: Pattern rejection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 544–549 (1996)

    Google Scholar 

  5. Bourdev, L.D., Brandt, J.: Robust object detection via soft cascade. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 236–243 (2005)

    Google Scholar 

  6. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.J.V.: Robust tracking-by-detection using a detector confidence particle filter. In: The IEEE Conf. on Computer Vision, pp. 1515–1522 (2009)

    Google Scholar 

  7. Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. International Journal of Computer Vision 77(1-3), 65–86 (2008)

    Article  Google Scholar 

  8. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  9. Crow, F.C.: Summed-area tables for texture mapping. In: SIGGRAPH, vol. 18, pp. 207–212 (1984)

    Google Scholar 

  10. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  11. Duda, R., Hart, P., Stork, D.: Pattern Classification, second edn. Wiley, New York (2001)

    MATH  Google Scholar 

  12. Elad, M., Hel-Or, Y., Keshet, R.: Pattern detection using a maximal rejection classifier. Pattern Recognition Letters 23(12), 1459–1471 (2002)

    Article  MATH  Google Scholar 

  13. Fleuret, F., Geman, D.: Coarse-to-fine face detection. International Journal of Computer Vision 41(1-2), 85–107 (2001)

    Article  MATH  Google Scholar 

  14. Froba, B., Ernst, A.: Face detection with the modified census transform. In: Proc of 16th IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 91–96 (2004)

    Google Scholar 

  15. Grabner, H., Bischof, H.: On-line boosting and vision. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 260–267 (2006)

    Google Scholar 

  16. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(9), 1263–1284 (2009)

    Google Scholar 

  17. Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(4), 671–686 (2007)

    Article  Google Scholar 

  18. Li, S.Z., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(9), 1112–1123 (2004)

    Article  Google Scholar 

  19. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: DAGM-Symposium, Lecture Notes in Computer Science, vol. 2781, pp. 297–304 (2003)

    Google Scholar 

  20. Liu, C., Shum, H.Y.: Kullback-leibler boosting. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. I, pp. 587–594 (2003)

    Google Scholar 

  21. Lu, W.L., Okuma, K., Little, J.J.: Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image and Vision Computing 27(1-2), 189–205 (2009)

    Article  Google Scholar 

  22. Masnadi-Shirazi, H., Vasconcelos, N.: Asymmetric boosting. In: Int Conf on Machine Learning (2007)

    Book  Google Scholar 

  23. Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)

    Article  Google Scholar 

  24. Mu, Y., Yan, S., Liu, Y., Huang, T., Zhou, B.: Discriminative local binary patterns for human detection in personal album. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  25. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: An application to face detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 130–136 (1997)

    Google Scholar 

  26. Paisitkriangkrai, S., Shen, C., Zhang, J.: Efficiently training a better visual detector with sparse eigenvectors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1129–1136. Miami, Florida (2009)

    Google Scholar 

  27. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: The IEEE Conf. on Computer Vision, pp. 555–562 (1998)

    Google Scholar 

  28. Pham, M.T., Cham, T.J.: Fast training and selection of haar features using statistics in boosting-based face detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2007)

    Book  Google Scholar 

  29. Pham, M.T., Hoang, V.D.D., Cham, T.J.: Detection with multi-exit asymmetric boosting. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2008)

    Book  Google Scholar 

  30. Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)

    Article  Google Scholar 

  31. Schapire, R., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statististics 26(5), 1651–1686 (1998)

    MathSciNet  MATH  Google Scholar 

  32. Schneiderman, H.: Feature-centric evaluation for efficient cascaded object detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 29–36 (2004)

    Google Scholar 

  33. Shen, C., Wang, P., Li, H.: LACBoost and FisherBoost: Optimally building cascade classifiers. In: European Conf. Computer Vision, vol. 2, pp. 608–621. Crete Island, Greece (2010)

    Google Scholar 

  34. Sun, J., Rehg, J., Bobick, A.: Automatic cascade training with perturbation bias. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 276–283 (2004)

    Google Scholar 

  35. Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(1), 39–51 (1998)

    Article  Google Scholar 

  36. Tieu, K., Viola, P.: Boosting image retrieval. International Journal of Computer Vision 56(1/2), 17–36 (2004)

    Article  Google Scholar 

  37. Torralba, A., Murphy, K., Freeman, W.: Sharing features: Efficient boosting procedures for multiclass object detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. II, pp. 762–769 (2004)

    Google Scholar 

  38. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: The IEEE Conf. on Computer Vision, vol. 2, pp. 1589–1596 (2005)

    Google Scholar 

  39. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  40. Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: Advances in Neural Information Processing Systems 14, pp. 1311–1318 (2002)

    Google Scholar 

  41. Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  42. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: The IEEE Conf. on Computer Vision, pp. 734–741 (2003)

    Google Scholar 

  43. Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: The IEEE Conf. on Computer Vision (2009)

    Book  Google Scholar 

  44. Webb, A.: Statistical Pattern Recognition. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  45. Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast asymmetric learning for cascade face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(3), 369–382 (2008)

    Article  Google Scholar 

  46. Wu, J., Geyer, C., Rehg, J.M.: Real-time human detection using contour cues. In: Proc. IEEE Int’l Conf. Robotics and Automation (2011)

    Book  Google Scholar 

  47. Wu, J., Mullin, M.D., Rehg, J.M.: Learning a rare event detection cascade by direct feature selection. In: Advances in Neural Information Processing Systems (NIPS) 16, pp. 1523–1530 (2004)

    Google Scholar 

  48. Wu, J., Mullin, M.D., Rehg, J.M.: Linear asymmetric classifier for cascade detectors. In: Int’l Conf. on Machine Learning, pp. 993–1000 (2005)

    Google Scholar 

  49. Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2007)

    Book  Google Scholar 

  50. Xiao, R., Zhu, L., Zhang, H.J.: Boosting chain learning for object detection. In: The IEEE Conf. on Computer Vision, pp. 709–715 (2003)

    Google Scholar 

  51. Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)

    Article  Google Scholar 

  52. Yang, M.H., Roth, D., Ahuja, N.: A snow-based face detector. In: Advances in Neural Information Processing Systems 12, pp. 862–868 (2000)

    Google Scholar 

  53. Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: International Conference on Biometrics, pp. 11–18 (2007)

    Google Scholar 

  54. Zhu, Q., Yeh, M.C., Cheng, K.T., Avidan, S.: Fast human detection using a cascade of histograms of oriented gradients. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, N4-2c-82 Nanyang Avenue, Singapore, 639798, Singapore

    Jianxin Wu

  2. School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    James M. Rehg

Authors
  1. Jianxin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James M. Rehg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Wu .

Editor information

Editors and Affiliations

  1. Microsoft, One Microsoft Road, Redmond, 98052, USA

    Cha Zhang

  2. Honeywell, Douglas Drive North 1985, Golden Valley, 55422, USA

    Yunqian Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Wu, J., Rehg, J.M. (2012). Object Detection. In: Zhang, C., Ma, Y. (eds) Ensemble Machine Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9326-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-9326-7_8

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-9325-0

  • Online ISBN: 978-1-4419-9326-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation