[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Occlusion detection using horizontally segmented windows for vehicle tracking

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposes an efficient algorithm for detecting occlusions in a video sequences of ground vehicles using color information. The proposed method uses a rectangular window to track a target vehicle, and the window is horizontally divided into several sub-regions of equal width. Each region is determined to be occluded or not based on the color histogram similarity to the corresponding region of the target. The occlusion detection results are used in likelihood computation of the conventional tracking algorithm based on particle filtering. Experimental results in real scenes show that the proposed method finds the occluded region successfully and improves the performance of the conventional trackers.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ablavsky V, Sclaroff S (2011) Layered graphical models for tracking partially occluded objects. IEEE Trans Pattern Anal Mach Intell 33:1758–1775

    Article  Google Scholar 

  2. Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: CVPR08, pp 1–8

  3. Arulampalam S, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–189

    Article  Google Scholar 

  4. Bao C, Wu Y, Ling H, Ji H (2012) Real time robust l1 tracker using accelerated proximal gradient approach. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1830–1837

  5. Beymer D, Konolige K (1999) Real-time tracking of multiple people using continuous detection. In Proceedings of the 7th international conference on computer vision, Kerkyra, Greece

  6. Bouttefroy PLM, Bouzerdoum A, Phung S, Beghdadi A (2009) Vehicle tracking using projective particle filter. In: IEEE international conference on advanced video and signal based surveillance (AVSS), pp 7–12

  7. Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Machine Intell 17:790–799

    Article  Google Scholar 

  8. Chu C-T, Hwang J-N, Pai H-I, Lan K-M (2011) Robust video object tracking based on multiple kernels with projected gradients. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1421–1424

  9. Comaniciu D, Meer P (1999) Mean shift analysis and applications. In: Proceedings of the 7th international conference on computer vision, Kerkyra, Greece, pp 1197–1203

  10. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Machine Intell 24(5):603–619

    Article  Google Scholar 

  11. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol II. Hilton Head, pp 142–149

  12. Comaniciu D, Ramesh V, Meer P (2003) Kernl-based object tracking. IEEE Trans Pattern Anal Machine Intell 25(5):564–575

    Article  Google Scholar 

  13. Cremers D, Kohlberger T, Schnorr C (2002) Non-linear shape statistics in mumford-shah based segmentation. In: Proceedings of the European conference on computer vision, Copenhagen, Denmark

  14. Dellaert F, Thorpe C (1998) Robust car tracking using Kalman filtering and Bayesian templates. In: Proceedings of SPIE, pp 72–83

  15. Foley JD, van Dam A, Feiner SK, Hughes J (1990) Computer graphics: principles and practice, 2nd edn. Addison-Wesley

  16. Hu W, Zhou X, Hu M, Maybank S (2009) Occlusion reasoning for tracking multiple people. IEEE Trans Circ Syst Video Technol 19:114–121

    Article  Google Scholar 

  17. Isard M, Blake A (1998) Condensation—Conditional density propagation for visual tracking. Intl J Comput Vis 29(1):5–28

    Article  Google Scholar 

  18. Isard M, MacCormick J (2001) Bramble: a bayesian multiple-blob tracker. In: Proceedings of the 8th international conference on computer vision, Vancouver, Canada, pp 34–41

  19. Jojic N, Caspi Y (2004) Capturing image structure with probabilistic index maps. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1. Washington DC. pp 212–219

  20. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans Am Soc Mech Eng D J Basic Eng 82:35–45

    Article  Google Scholar 

  21. Kwak S, Nam W, Han B, Han JH (2011) Learning occlusion with likelihoods for visual tracking. In: Proceedings of the 14th international conference on computer vision, Barcelona, Spain, pp 1551–1558

  22. Kwak S, Nam W, Han B, Han JH (2011) Learning occlusion with likelihoods for visual tracking. In: 2011 IEEE international conference on computer vision (ICCV), pp 1551–1558

  23. Lee K-H, Hwang J-N, Yu J-Y, Lee K-Z (2013) Vehicle tracking iterative by kalman-based constrained multiple-kernel and 3-d model-based localization. In: IEEE international symposium on circuits and Systems (ISCAS), pp 2396–2399

  24. MacKay DJC (1998) Introduction to Monte Carlo methods. In: Learning in graphical models. Kluwer Academic Press, pp 175–204

  25. Perez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. In: Proceedings European conference on computer vision, vol I. Copenhagen, Denmark, pp 661–675

  26. Perez P, Vermaak J, Blake A (2004) Data fusion for visual tracking with particle filter. Proc IEEE 92(3):495–513

    Article  Google Scholar 

  27. Quast K, Kaup A (2013) Shape adaptive mean shift object tracking using gaussian mixture models. In: Adami N, Cavallaro A, Leonardi R, Migliorati P (eds) Lecture notes in electrical engineering, analysis, retrieval and delivery of multimedia content, vol 158. Springer, New York, pp 107–122

    Chapter  Google Scholar 

  28. Salarpour A, Salarpour A, Fathi M, Dezfoulian M (2011) Vehicle tracking using Kalman filter and features. Signal Image Proc Int J (SIPIJ) 2:1–8

    Article  Google Scholar 

  29. Scharcanski J, de Oliveira AB, Cavalcanti PG, Yari Y (2011) A particle-filtering approach for vehicular tracking adaptive to occlusions. IEEE Trans Veh Technol 60:381–389

    Article  Google Scholar 

  30. Stenger B, Mendonça PRS, Cipolla R (2001) Model-based hand tracking using an unscented kalman filter. In: Proceedings of the British machine vision conference, vol I. Manchester, UK, pp 63–72

  31. Sudderth EB, Mandel MI, Freeman WT, Willsky AS (2005) Distributed occlusion reasoning for tracking with nonparametric belief propagation. In: Advances in neural information processing systems, pp 1369–1376

  32. Tanizaki H (2000) Nonlinear and non-gaussian state-space modeling with Monte Carlo techniques: a survey and comparative study. North-Holland

  33. Tanizaki H, Mariano RS (1998) Nonlinear and non-gaussian state-space modeling with Monte-Carlo simulations. J Econ 83(1–2):263–290

    Article  MATH  Google Scholar 

  34. Wu B, Nevatia R (2007) Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Intl J Comput Vis 75(2):247–266

    Article  Google Scholar 

  35. Wu Y, Yu T, Hua G (2003) Tracking appearances with occlusions. In: CVPR03, vol 1. pp 789–795

  36. Xiaojing Zhang CS, Yue Y (2013) Object tracking approach based on mean shift algorithm. J Multimedia 8(3):220–225

    Google Scholar 

  37. Xiong T, Debrunner C (2004) Stochastic car tracking with line- and color-based features. IEEE Trans Intell Transp Syst 5(4):324–328

    Article  Google Scholar 

  38. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38:263–290

    Article  Google Scholar 

  39. Yilmaz A, Li X, Shah M (2004) Contour based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans Pattern Anal Machine Intell 26(11):1531–1536

    Article  Google Scholar 

  40. Zhang Z, Huang K, Tan T, Wang Y (2010) 3d model based vehicle tracking using gradient based fitness evaluation under particle filter framework. In: international conference on pattern recognition (ICPR), pp 1771–1774

  41. Zhong J, Sclaroff S (2003) Segmenting foreground objects from a dynamic textured background via a robust kalman filter. In Proceedings of the 9th international conference on computer vision. Nice, France, pp 44–50

  42. Zhou S, Chellappa R, Moghaddam B (2004) Visual tracking and recognition using appearance adaptive models in particle filters. IEEE Trans Image Process 11:1434–1456

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 689-798, Republic of Korea

    Ahra Jo & Gil-Jin Jang

  2. Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Republic of Korea

    Bohyung Han

Authors
  1. Ahra Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gil-Jin Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bohyung Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gil-Jin Jang.

Additional information

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (No. 2012-0008090), and by the Converging Research Center Program through the Ministry of Science, ICT and Future Planning, Korea (No. 2013K000359).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jo, A., Jang, GJ. & Han, B. Occlusion detection using horizontally segmented windows for vehicle tracking. Multimed Tools Appl 74, 227–243 (2015). https://doi.org/10.1007/s11042-013-1846-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1846-5

Keywords

Search

Navigation