Skip to main content
SpringerLink
Log in

Moving Object Detection Using SIFT Matching on Three Frames for Advanced Driver Assistance Systems

  • Conference paper
  • First Online:
Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

Included in the following conference series:

  • 1471 Accesses

Abstract

Detecting moving objects in a dynamic scene is a difficult task in computer vision. We propose a moving object detection algorithm for advanced driver assistance systems that uses only images from a monocular camera. To distinguish moving objects from standing objects when the camera is moving, we used an epipolar line constraint and an optical flow constraint. When evaluated using the KITTI scene flow 2015 dataset, the proposed algorithm detected moving objects in the image successfully even when the monocular camera was moving. The runtime of the proposed algorithm is < 1 s, so it is feasible for practical uses.

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

References

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

    Google Scholar 

  2. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  3. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  4. Fortun, D., Bouthemy, P., Kervrann, C.: Optical flow modeling and computation: a survey. Comput. Vis. Image Underst. 134, 1–21 (2015)

    Article  Google Scholar 

  5. Fu, H., Xu, D., Zhang, B., Lin, S.: Object-based multiple foreground video co-segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3166–3173. IEEE (2014)

    Google Scholar 

  6. Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000). doi:10.1007/3-540-45053-X_3

    Chapter  Google Scholar 

  7. Geronimo, D., Lopez, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 7, 1239–1258 (2009)

    Google Scholar 

  8. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, p. 50. Citeseer (1988)

    Google Scholar 

  9. Hartley, R.I.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)

    Article  Google Scholar 

  10. Horn, B.K., Schunck, B.G.: Determining optical flow. In: 1981 Technical Symposium East, pp. 319–331. International Society for Optics and Photonics (1981)

    Google Scholar 

  11. Jun, W.: MOD for collision warning using monocular camera in dynamic scene. Master’s thesis, Pohang University of Science and Technology (2016)

    Google Scholar 

  12. Jun, W., Ha, J., Jeong, H.: Moving object detection using energy model and particle filter for dynamic scene. In: Bräunl, T., McCane, B., Rivera, M., Yu, X. (eds.) PSIVT 2015. LNCS, vol. 9431, pp. 111–122. Springer, Heidelberg (2016). doi:10.1007/978-3-319-29451-3_10

    Chapter  Google Scholar 

  13. Klappstein, J., Stein, F., Franke, U.: Monocular motion detection using spatial constraints in a unified manner. In: 2006 IEEE Intelligent Vehicles Symposium, pp. 261–267. IEEE (2006)

    Google Scholar 

  14. Klappstein, J., Vaudrey, T., Rabe, C., Wedel, A., Klette, R.: Moving object segmentation using optical flow and depth information. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 611–623. Springer, Heidelberg (2009). doi:10.1007/978-3-540-92957-4_53

    Chapter  Google Scholar 

  15. Kwak, S., Lim, T., Nam, W., Han, B., Han, J.H.: Generalized background subtraction based on hybrid inference by belief propagation and bayesian filtering. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2174–2181. IEEE (2011)

    Google Scholar 

  16. Lee, D.S.: Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005)

    Article  Google Scholar 

  17. Lenz, P., Ziegler, J., Geiger, A., Roser, M.: Sparse scene flow segmentation for moving object detection in urban environments. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 926–932. IEEE (2011)

    Google Scholar 

  18. Lim, J., Han, B.: Generalized background subtraction using superpixels with label integrated motion estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 173–187. Springer, Heidelberg (2014)

    Google Scholar 

  19. Lindström, M., Eklundh, J.O.: Detecting and tracking moving objects from a mobile platform using a laser range scanner. In: Proceedings of 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, vol. 3, pp. 1364–1369. IEEE (2001)

    Google Scholar 

  20. Longuet-Higgins, H.C., Prazdny, K.: The interpretation of a moving retinal image. Proc. R. Soc. Lond. B: Biol. Sci. 208(1173), 385–397 (1980)

    Article  Google Scholar 

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  22. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061–3070 (2015)

    Google Scholar 

  23. Mertz, C., Navarro-Serment, L.E., MacLachlan, R., Rybski, P., Steinfeld, A., Suppe, A., Urmson, C., Vandapel, N., Hebert, M., Thorpe, C., et al.: Moving object detection with laser scanners. J. Field Rob. 30(1), 17–43 (2013)

    Article  Google Scholar 

  24. Mumtaz, A., Zhang, W., Chan, A.B.: Joint motion segmentation and background estimation in dynamic scenes. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 368–375. IEEE (2014)

    Google Scholar 

  25. Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single-frame classification and system level performance. In: 2004 IEEE Intelligent Vehicles Symposium, pp. 1–6. IEEE (2004)

    Google Scholar 

  26. Shimada, A., Nagahara, H., Taniguchi, R.I.: Background modeling based on bidirectional analysis. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1979–1986. IEEE (2013)

    Google Scholar 

  27. Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)

    Article  Google Scholar 

  28. Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)

    Article  Google Scholar 

  29. Yamaguchi, K., Kato, T., Ninomiya, Y.: Vehicle ego-motion estimation and moving object detection using a monocular camera. In: 18th International Conference on Pattern Recognition, 2006, ICPR 2006, vol. 4, pp. 610–613. IEEE (2006)

    Google Scholar 

  30. Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the Human Resource Training Program for Regional Innovation and Creativity through the Ministry of Education and National Research Foundation of Korea.

Author information

Authors and Affiliations

  1. POSTECH, Pohang, South Korea

    Jeongmok Ha & Hong Jeong

  2. VADAS Co., Ltd., Pohang, South Korea

    WooYeol Jun

Authors
  1. Jeongmok Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. WooYeol Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeongmok Ha .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Ha, J., Jun, W., Jeong, H. (2016). Moving Object Detection Using SIFT Matching on Three Frames for Advanced Driver Assistance Systems. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46418-3_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation