Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Multimedia Systems
Published in: Multimedia Systems 6/2018

12-02-2018 | Regular Paper

Multiple player tracking in soccer videos: an adaptive multiscale sampling approach

Authors: Wonjun Kim, Sung-Won Moon, Jiwon Lee, Do-Won Nam, Chanho Jung

Published in: Multimedia Systems | Issue 6/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Visual tracking is an essential technique in computer vision. Even though the notable improvement has been achieved during last few years, tracking multiple objects still remains as a challenging task. In this paper, a novel method for tracking multiple players in soccer videos, which include severe occlusions between players and nonlinear motions by their complex interactions, is introduced. Specifically, we first extract moving objects (i.e., players) by refining results of background subtraction via the edge information obtained from the frame differencing result. Then, we conduct multiscale sampling in foreground regions, which are spatially close to each tracked player, and subsequently computing the dissimilarity between sampled image blocks and each tracked player. Based on the best-matched case, the state of each tracked player (e.g., center position, color, etc.) is consistently updated using the online interpolation scheme. Experimental results in various soccer videos show the efficiency and robustness of our method compared to previous approaches introduced in the literature.
next article Association rule mining for the usability of the CAPTCHA interfaces: a new study of multimedia systems

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Appendix
Available only for authorised users
Literature
1.
Feris, R.S., Siddiquie, B., Petterson, J., Zhai, Y., Datta, A., Brown, L.M., Pankanti, S.: Large-scale vehicle detection, indexing, and search in urban surveillance videos. IEEE Trans. Multimed. 14(1), 28–42 (2012)CrossRef
2.
Chu, C.-T., Hwang, J.-N., Pai, H.-I., Lan, K.-M.: Tracking human under occlusion based on adaptive multiple kernels with projected gradients. IEEE Trans. Multimed. 15(7), 1602–1615 (2013)CrossRef
3.
Chang, C.-H., Wang, S.-C., Wang, C.-C.: Exploiting moving objects: multi-robot simultaneous localization and tracking. IEEE Trans. Auto. Sci. Eng. 13(2), 810–827 (2016)CrossRef
4.
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of international conference on pattern recognition (ICPR), pp. 28–31 (2004)
5.
Kim, c, Hwang, J.-N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Trans. Circ. Syst. Video Technol 12(2), 122–129 (2002)CrossRef
6.
Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME J. Basic Eng. 82, 35–45 (1960)CrossRef
7.
Xu, M., Orwell, J., Jones, G.: Tracking football players with multiple cameras. In: Proceedings of IEEE international conference on image processing (ICIP), pp. 2909–2912 (2004)
8.
Iwase, S., Saito, H.: Parallel tracking of all soccer players by integrating detected positions in multiple view images. In: Proceeding of international conference on pattern recognition (ICPR), pp 751–754 (2004)
9.
Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multiple people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)CrossRef
10.
Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Multi-commodity network flow for tracking multiple people. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1614–1627 (2014)CrossRef
11.
Hu, M.-C., Chang, M.-H., Wu, J.-L., Chi, L.: Robust camera calibration and player tracking in broadcast basketvall video. IEEE Trans. Multimed. 13(2), 266–279 (2011)CrossRef
12.
Li, H., Flierl, M.: Sift-based multi-view cooperative tracking for soccer video. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1001–1004 (2012)
13.
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRef
14.
Liu, J., Tong, X., Li, W., Wang, T., Zhang, Y., Wang, H.: Automatic player detection, labeling, and tracking in broadcast soccer video. Pattern Recognit. Lett. 30, 103–113 (2009)CrossRef
15.
Xing, J., Ai, H., Liu, L., Lao, S.: Multiple player tracking in sports video: a dual-mode two-way bayesian inference approach with progressive observation modeling. IEEE Trans. Image Process. 20(6), 1652–1666 (2011)MathSciNetCrossRef
16.
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806–1818 (2011)CrossRef
17.
Zhang, T., Ghanem, B., Ahuja, N.: Robust multi-object tracking via cross-domain contextual information for sports video analysis. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 985–988 (2012)
18.
Liu, J., Carr, P., Collins, R., Liu. Y.: Tracking sports players with context-conditioned motion models. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 1830–1837 (2013)
19.
Lu, W.-L., Ting, J.-A., Little, J.J., Murphy, K.P.: Learning to track and identify players from broadcast sports videos. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1704–1716 (2013)CrossRef
20.
Baysal, S., Duygulu, P.: Sentioscope: a soccer player tracking system using model field particles. IEEE Trans. Circuits Syst. Video Technol. 26(7), 1350–1362 (2016)CrossRef
21.
Wang, L., Ouyang, W., Wang, X., Lu., H.: Visual tracking with fully convolutional networks. In: Proceeding of IEEE international conference on computer vision (ICCV). pp 3119–3127 (2015)
22.
Held, D., Thrun, S., Savarese, S.: Learning to track at 100fps with deep regression networks. In: Proceedings of European conference on computer vision (ECCV), pp. 749–765 (2016)CrossRef
23.
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRef
24.
Han, J., Ma, K.-K.: Fuzzy color histogram and its use in color image retrieval. IEEE Trans. Image Process. 11(8), 944–952 (2002)CrossRef
25.
Varior, R. R., Wang, G.: A data-driven color feature learning scheme for image retrieval. In: Proceeding of IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1334–1338 (2015)
26.
Henriques, J. F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceeding of European conference on computer vision (ECCV), pp. 1–14 (2012)CrossRef
27.
D’Orazio, T., Leo, M., Mosca, N., Spagnolo, P., Mazzeo, P. L.: A semi-automatic system for ground truth generation of soccer video sequences. In: Proceeding of IEEE international conference on advanced video and signal based surveillance (AVSS), pp. 559–564 (2009)
28.
Stauffer, C., Grimson, W. E. L.: Adaptive background mixture models for real-time tracking. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 246–252 (1999)
29.
Kim, W., Kim, Y.: Background subtraction using illumination-invariant structural complexity. IEEE Signal Process. Lett. 23(5), 634–638 (2016)CrossRef
30.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012)CrossRef
31.
Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proceeding of British machine vision conference (BMVC), pp. 47–56 (2006)
32.
Babenko, B., Yang, M-H., Belongie, S.: Visual tracking with online multiple instance learning. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 983–990 (2009)
33.
Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: Proceeding of international conference on pattern recognition (ICPR), pp. 2756–2759 (2010)
34.
Danelljan, M., Khan, F. S., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 1090–1097 (2014)
35.
Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detector. Int. J. Comput. Vis. 75(2), 247–266 (2007)CrossRef
36.
Xing, J., Ai, H., Lao, S.: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 1200–1207 (2009)
37.
Wu, Y., Lim, J., Yang, M. H.: Online object tracking: a benchmark. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 2411–2418 (2013)
38.
Pirsiavash, H., Ramanan, D., Fowlkes, C. C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: Proceeding of IEEE international conference on computer vision and pattern recognition (CVPR), pp. 1201–1208 (2011)
Metadata
Title
Multiple player tracking in soccer videos: an adaptive multiscale sampling approach
Authors
Wonjun Kim
Sung-Won Moon
Jiwon Lee
Do-Won Nam
Chanho Jung
Publication date
12-02-2018
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2018
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-018-0586-9

Other articles of this Issue 6/2018

Multimedia Systems 6/2018 Go to the issue

Regular Paper

Securing JPEG-2000 images in constrained environments: a dynamic approach

Regular Paper

Modelling user quality of experience from objective and subjective data sets using fuzzy logic

Regular Paper

Smell and touch in the Virtual Jumpcube

Regular Paper

Association rule mining for the usability of the CAPTCHA interfaces: a new study of multimedia systems