nach oben

Erschienen in:

2015 | OriginalPaper | Buchkapitel

A Fast Object Detecting-Tracking Method in Compressed Domain

verfasst von : Zenglei Qian, Jiuzhen Liang, Zhiguo Niu, Yongcun Xu, Qin Wu

Erschienen in: Computer Vision - ACCV 2014 Workshops

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Background Subtraction: Model-Sharing Strategy Based on Temporal Variation Analysis

Nächstes Kapitel Automatic RoI Detection for Camera-Based Pulse-Rate Measurement

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)

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)

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)CrossRef

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)CrossRef

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)

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

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

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)CrossRef

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)

10.

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

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) CrossRef

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)CrossRefMathSciNet

13.

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

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)

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)CrossRef

16.

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

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)

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)

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)CrossRef

20.

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

21.

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

22.

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

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)CrossRef

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) CrossRef

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)

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

27.

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

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) CrossRef

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)

30.

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

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) CrossRef

32.

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

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)

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)CrossRef

Titel: A Fast Object Detecting-Tracking Method in Compressed Domain
verfasst von: Zenglei Qian
Jiuzhen Liang
Zhiguo Niu
Yongcun Xu
Qin Wu
Verlag: Springer International Publishing
Buch: Computer Vision - ACCV 2014 Workshops
Print ISBN: 978-3-319-16630-8

Electronic ISBN: 978-3-319-16631-5

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-16631-5_26

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"