Skip to main content
SpringerLink
Log in

A Fast Object Detecting-Tracking Method in Compressed Domain

  • Conference paper
  • First Online:
Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

Included in the following conference series:

  • 2026 Accesses

Abstract

The traditional pixel domain tracking algorithms are often applied to rigid objects which move slowly in simple background. But it performs very poor for non-rigid object tracking. In order to solve this problem, this paper proposes a tracking method of rapid detection in compressed domain. Convex hull formed by Self-adaptive boundary searching method and rule-based clustering are adopted for the detector in order to reduce the complexity of the algorithm. At the tracking stage, Kalman filtering is used to forecast the location of the objective. Meanwhile, as the whole process is completed in the compressed domain, it can meet the real-time requirement compared with other algorithms. And it tracks the target more precisely. The experimental results show that the proposed method has the following properties: (1) more advantages in tracking small-sized objects; (2) a better effect when track a fast moving objects; (3) faster tracking speed.

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. Babenko, B., Yang, M.-H., Belongie, S.: Visual tracking with online multiple instance learning. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 983–990 (2009)

    Google Scholar 

  2. Chen, K., Zhao, X., Xu, T., Napolitano, M.R.: Enhanced strong Kalman filter applied in precise video tracking for fast mobile target. In: 2010 IEEE International Conference on Progress in Informatics and Computing (PIC), vol. 2, pp. 875–878 (2010)

    Google Scholar 

  3. Chen, Y.-M., Bajic, I.V.: A joint approach to global motion estimation and motion segmentation from a coarsely sampled motion vector field. IEEE Trans. Circ. Syst. Video Technol. 9, 1316–1328 (2011)

    Article  Google Scholar 

  4. Chen, Y.-M., Bajic, I.V., Saeedi, P.: Moving region segmentation from compressed video using global motion estimation and Markov random fields. IEEE Trans. Multimedia 3, 421–431 (2011)

    Article  Google Scholar 

  5. Chen, Y.-M., Bajic, I.V., Saeedi, P.: Motion segmentation in compressed video using Markov random fields. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 760–765 (2010)

    Google Scholar 

  6. Doucet, A., De Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)

    Book  MATH  Google Scholar 

  7. Fei, W., Zhu, S.: Mean shift clustering-based moving object segmentation in the H.264 compressed domain. IET Image Process. 4, 11–18 (2010)

    Article  Google Scholar 

  8. Fang, Y., Lin, W., Chen, Z., Tsai, C., Lin, C.: A video saliency detection model in compressed domain. IEEE Trans. Circuits Syst. Video Technol. 24(1), 27–38 (2014)

    Article  Google Scholar 

  9. Huang, S., Hong, J.: Moving object tracking system based on camshift and Kalman filter. In: 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 1423–1426 (2011)

    Google Scholar 

  10. Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 3, 401–422 (2004)

    Article  Google Scholar 

  11. Käs, C., Nicolas, H.: An approach to trajectory estimation of moving objects in the H.264 compressed domain. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 318–329. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  12. Khatoonabadi, S.H., Bajic, I.V.: Video object tracking in the compressed domain using spatio-temporal Markov random fields. IEEE Trans. Image Process. 1, 300–313 (2013)

    Article  MathSciNet  Google Scholar 

  13. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)

    Article  Google Scholar 

  14. Liu, Z., Lu, Y., Zhang, Z.: Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain. J. Vis. Commun. Image Represent. 3, 375–290 (2007)

    Google Scholar 

  15. Liu, Z., Lu, Y., Zhang, Z.: An efficient compressed domain moving object segmentation algorithm based on motion vector field. J. Shanghai Univ. (Engl. Edn.) 12, 221–227 (2008)

    Article  Google Scholar 

  16. Liu, Z., Zhang, Z., Shen, L.: Moving object segmentation in the H.264 compressed domain. Opt. Eng. 1, 017003 (2007)

    MathSciNet  Google Scholar 

  17. Maekawa, E., Goto, S.: Examination of a tracking and detection method using compressed domain information. In: 2013 Picture Coding Symposium (PCS). Waseda University, Shinjuku-ku, Tokyo, Japan, pp. 141–144 (2013)

    Google Scholar 

  18. Moura, R.C., Hemerly, E.M.: A spatiotemporal motion-vector filter for object tracking on compressed video. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 427–434 (2010)

    Google Scholar 

  19. Mezaris, V., Kompatsiaris, I., Boulgouris, N.V., Strintzis, M.G.: Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval. IEEE Trans. Circ. Syst. Video Technol. 14, 606–621 (2004)

    Article  Google Scholar 

  20. Rhodes, I.B.: A tutorial introduction to estimation and filtering. IEEE Trans. Autom. Control 6, 688–706 (1971)

    Article  MathSciNet  Google Scholar 

  21. Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, Hoboken (2006)

    Book  Google Scholar 

  22. Salmond, D.: Target tracking: introduction and Kalman tracking filters. Target Tracking: Algorithms and Applications (Ref. No. 2001/174), IEE, p. 1 (2011)

    Google Scholar 

  23. Sabirin, H., Kim, M.: Moving object detection and tracking using a spatio-temporal graph in H.264/AVC bitstreams for video surveillance. IEEE Trans. Multimedia 3, 657–668 (2012)

    Article  Google Scholar 

  24. Treetasanatavorn, S., Rauschenbach, U., Heuer, J., Kaup, A.: Bayesian method for motion segmentation and tracking in compressed videos. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 277–284. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Treetasanatavorn, S., Rauschenbach, U., Heuer, J., Kaup, A.: Model based segmentation of motion fields in compressed video sequences using partition projection and relaxation. In: 2005 Visual Communications and Image Processing, p. 59600D (2005)

    Google Scholar 

  26. Treetasanatavorn, S., Rauschenbach, U., Heuer, J., Kaup, A.: Stochastic motion coherency analysis for motion vector field segmentation on compressed video sequences. In: 2005 Proceedings of WIAMIS, April 2005

    Google Scholar 

  27. Wu, Y., Jia, N., Sun, J.: Real-time multi-scale tracking based on compressive sensing. Vis. Comput. 31(4), 471–484 (2014). Springer

    Article  Google Scholar 

  28. You, W., Houari Sabirin, M.S., Kim, M.: Moving object tracking in H.264/AVC bitstream. In: Sebe, N., Liu, Y., Zhuang, Y., Huang, T.S. (eds.) MCAM 2007. LNCS, vol. 4577, pp. 483–492. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. You, W., Sabirin, M.S.H., Kim, M.: Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain. In: Proceedings of SPIE, vol. 7244, pp. 72440D–72440D-12 (2009)

    Google Scholar 

  30. Zhang, K., Song, H.: Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn. 1, 397–411 (2013)

    Article  Google Scholar 

  31. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  32. Zhang, K., Zhang, L., Yang, M.: Fast Compressive Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2015)

    Article  Google Scholar 

  33. Zhang, K., Zhang, L., Yang, M.-H., Zhang, D.: Fast Tracking via Spatio-Temporal Context Learning (2013). arXiv preprint arXiv:1311.1939

  34. Zhang, L., Chew, Y.H., Wong, W.-C.: A novel angle-of-arrival assisted extended Kalman filter tracking algorithm with space-time correlation based motion parameters estimation. In: 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1283–1289 (2013)

    Google Scholar 

  35. Zeng, W., Du, J., Gao, W., Huang, Q.: Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imag. 4, 290–299 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Internet of Things Engineering College, Jiangnan University, Binhu District, Wuxi, Jiangsu Province, China

    Zenglei Qian, Jiuzhen Liang, Zhiguo Niu, Yongcun Xu & Qin Wu

Authors
  1. Zenglei Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiuzhen Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiguo Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongcun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiuzhen Liang .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Qian, Z., Liang, J., Niu, Z., Xu, Y., Wu, Q. (2015). A Fast Object Detecting-Tracking Method in Compressed Domain. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16631-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation