Skip to main content

Object Tracking and Identification in Video Streams with Snakes and Points

  • Conference paper
  • 1180 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3333))

Abstract

This paper presents a generic approach for object tracking and identification in video sequences, called SAP. The object is described with two image primitives: first, its content is described with Points of interest that are automatically extracted and characterized according to an appearance-based model. Second, the object’s envelope is described with a Snake. The originality of SAP consists in a complementary use of these primitives: the snake allows to reduce the points extraction to a limited area, and the point description is efficiently exploited during the snake tracking. Such a characterization is robust to wide occlusions and can be use for object identification and localization purposes. SAP has been implemented with the aim of achieving near real-time performance.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)

    Google Scholar 

  2. Castellano, G., Boyce, J., Sandler, M.: Regularized cdwt optical flow applied to moving-target detection in IR imagery. Machine Vision and Applications 11(6), 277–288 (2000)

    Article  Google Scholar 

  3. Chesnaud, C., Réfrégier, P., Boulet, V.: Statistical region snake-based segmentation adapted to different physical noise models. IEEE PAMI 21(11), 1145–1157 (1999)

    Google Scholar 

  4. Chetverikov, D., Verestóy, J.: Tracking feature points: A new algorithm. In: Proc. International Conf. on Pattern Recognition, pp. 1436–1438 (1998)

    Google Scholar 

  5. Florack, L.M.J., ter Haar Romeny, B.M., Koenderink, J.J., Viergever, M.A.: General intensity transformations and differential invariants. Journal of Mathematical Imaging and Vision 4(2), 171–187 (1994)

    Article  MathSciNet  Google Scholar 

  6. Gouet, V., Boujemaa, N.: Object-based queries using color points of interest. In: IEEE Workshop CBAIVL, Kauai, Hawaii, USA, pp. 30–36 (2001)

    Google Scholar 

  7. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  8. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contours models. International Journal of Computer Vision, 321–331 (1988)

    Google Scholar 

  9. Kim, M., Jeon, J.G., Kwak, J.S., Lee, M.H., Ahn, C.: Moving object segmentation in video sequences by user interaction and automatic object tracking. IVC 19(5), 245–260 (2001)

    Google Scholar 

  10. Koenderink, J.J., Van Doorn, A.J.: Representation of local geometry int the visual system. Biological Cybernetics 55, 367–375 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Accepted for publication int the International Journal of Computer Vision (2004)

    Google Scholar 

  12. Megret, R., Jolion, J.M.: Tracking scale-space blobs for video description. IEEE Multimedia 9(2), 34–43 (2002)

    Article  Google Scholar 

  13. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. Intl. Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  14. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: ICCV 2001, Vancouver, Canada (July 2001)

    Google Scholar 

  15. Montesinos, P., Gouet, V., Deriche, R.: Differential Invariants for Color Images. In: ICPR 1998, Brisbane, Australia (1998)

    Google Scholar 

  16. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Salari, V., Sethi, I.K.: Feature point correspondence in the presence of occlusion. IEEE PAMI 12(1), 56–73 (1990)

    Google Scholar 

  18. Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE PAMI 19(5), 530–534 (1997)

    Google Scholar 

  19. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  20. Sethi, I.K., Jain, R.: Finding trajectories of feature points in a monocular image sequence. IEEE PAMI 9, 56–73 (1987)

    Google Scholar 

  21. Veenman, C.J., Hendriks, E.A., Reinders, M.J.T.: A fast and robust point tracking algorithm. In: International Conference in Images Processing (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lameyre, B., Gouet, V. (2004). Object Tracking and Identification in Video Streams with Snakes and Points. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30543-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23985-7

  • Online ISBN: 978-3-540-30543-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics