Skip to main content
SpringerLink
Log in

Local Temporal Coherence for Object-Aware Keypoint Selection in Video Sequences

  • Conference paper
  • First Online:
Book cover Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10736))

Included in the following conference series:

  • 2319 Accesses

Abstract

Local feature extraction is an important solution for video analysis. The common framework of local feature extraction consists of a local keypoint detector and a keypoint descriptor. Existing keypoint detectors mainly focus on the spatial relationships among pixels, resulting in a large amount of redundant keypoints on background which are often temporally stationary. This paper proposes an object-aware local keypoint selection approach to keep the active keypoints on object and to reduce the redundant keypoints on background by exploring the temporal coherence among successive frames in video. The proposed approach is made up of three local temporal coherence criteria: (1) local temporal intensity coherence; (2) local temporal motion coherence; (3) local temporal orientation coherence. Experimental results on two publicly available datasets show that the proposed approach reduces more than 60% keypoints, which are redundant, and doubles the precision of keypoints.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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.

    www.cvg.reading.ac.uk/PETS2001/pets2001-dataset.html.

  2. 2.

    www.gti.ssr.upm.es/data/lasiesta_database.html.

  3. 3.

    limu.ait.kyushu-u.ac.jp/dataset/.

References

  1. Fan, B., Wang, Z., Wang, F.: Local Image Descriptor: Modern Approaches. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-49173-7

    Book  MATH  Google Scholar 

  2. Awad, A.I., Hassaballah, M.: Image Feature Detectors and Descriptors. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-28854-3

    Book  Google Scholar 

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

    Article  Google Scholar 

  4. Harris, C., Stephens, M.: A combined coer and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  5. Beaudet, P.: Rotationally invariant image operators. In: International Conference on Pattern Recognition, pp. 579–583 (1978)

    Google Scholar 

  6. Smith, S.M., Brady, J.M.: SUSAN: a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)

    Article  Google Scholar 

  7. Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: International Conference on Computer Vision, pp. 1508–1515 (2005)

    Google Scholar 

  8. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_34

    Chapter  Google Scholar 

  9. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)

    Article  Google Scholar 

  10. Zhang, H.J., Wu, J., Zhong, D., Smoliar, S.: An integrated system for content-based video retrieval and browsing. Pattern Recognit. 30(4), 643–658 (1997)

    Article  Google Scholar 

  11. Laptev, I., Lindeberg, T.: Space-time interest points. In: International Conference on Computer Vision, pp. 432–439 (2003)

    Google Scholar 

  12. Noguchi, A., Yanai, K.: Extracting spatio-temporal local features considering consecutiveness of motions. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 458–467. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12304-7_43

    Chapter  Google Scholar 

  13. Baroffio, L., Cesana, M., Redondi, A., Tagliasacchi, M., Tubaro, S.: Fast keypoint detection in video sequences. In: International Conference on Acoustics, Speech and Signal Processing, pp. 1342–1346 (2016)

    Google Scholar 

  14. Huong, V.T.L., Park, D.-C., Woo, D.M., Lee, Y.: Centroid neural network with Chi square distance measure for texture classification. In: International Joint Conference on Neural Networks, pp. 1310–1315 (2009)

    Google Scholar 

  15. Cuevas, C., Yáñez, E.M., García, N.: Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA. Comput. Vis. Image Underst. 152, 103–117 (2016)

    Article  Google Scholar 

  16. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  17. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_56

    Chapter  Google Scholar 

  18. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

Download references

Acknowledgments

This work was supported by KAKENHI (16K13006) and Waseda University Grant for Special Research Projects (2017K-263).

Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, 808-0135, Japan

    Songlin Du & Takeshi Ikenaga

Authors
  1. Songlin Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takeshi Ikenaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Songlin Du .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Du, S., Ikenaga, T. (2018). Local Temporal Coherence for Object-Aware Keypoint Selection in Video Sequences. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77383-4_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77382-7

  • Online ISBN: 978-3-319-77383-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation