Skip to main content
Log in

Adaptive relay detection using primary and auxiliary detectors for tracking

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

Abstract

A tracking method based on adaptive relay detection using primary and auxiliary detectors is proposed. In this framework, the tracking problem is formulated as the continuous relay detection, where primary detector and auxiliary detectors collaborate to locate the target and are updated online. Each of the detectors corresponds to one of the appearances of target that have appeared. Primary detector that always corresponds to the current appearance of target is set to conduct object detection, while auxiliary detectors that correspond to the previous appearances of target are used to re-detect the target when it shows a previous appearance. To achieve better classification with less features and ferns, the detectors are constructed based on the feature selection by the mutual information. As the previous appearances of target are recorded by the detectors correspondingly and only primary detector needs to update, our tracker can achieve long-term real-time object tracking in unconstrained environments. Experimental results on challenging real-world video sequences demonstrate that our tracker outperforms most of the state-of-the-art methods.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. IEEE Conf Comput Vis Pattern Recognit (CVPR) 798–805

  2. Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072

    Article  Google Scholar 

  3. Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271

    Article  Google Scholar 

  4. Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learning. IEEE Conf Comput Vis Pattern Recognit (CVPR) 983–990

  5. Belagiannis V, Schubert F, Navab N, Ilic S (2012) Segmentation based particle filtering for real-time 2d object tracking. Eur Conf Comput Vis (ECCV) 842–855

  6. Burt P J, Yen C, Xu X (1982) Local correlation measures for motion analysis: a Comparitive Study. IEEE Conf Pattern Recognit Image Process 269–274

  7. Collins R, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643

    Article  Google Scholar 

  8. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  9. Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1177–1184

  10. Duffner S, Garcia C (2013) PixelTrack: a fast adaptive algorithm for tracking non-rigid objects. IEEE Int’l Conf Comput Vis (ICCV) 2480–2487

  11. Fan J, Shen X, Wu Y (2012) Scribble tracker: a matting-based approach for robust tracking. IEEE Trans Pattern Anal Mach Intell 34(8):1633–1644

    Article  Google Scholar 

  12. Gall J, Yao A, Razavi N, Gool LV, Lempitsky V (2011) Hough forests for object detection, tracking, and action recognition. IEEE Trans Pattern Anal Mach Intell 33(11):2188–2202

    Article  Google Scholar 

  13. Godec M, Sternig S, Roth PM, Bischof H (2010) Context-driven clustering by multi-class classification in an active learning framework. IEEE Conf Comput Vis Pattern Recognit Work (CVPRW) 19–24

  14. Grabner H, Bischof H (2006) On-line boosting and vision. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1:260–267

    Google Scholar 

  15. Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. IEEE Int’l Conf Comput Vis (ICCV) 263–270

  16. Hua Y, Alahari K, Schmid C (2014) Occlusion and motion reasoning for long-term tracking. Eur Conf Comput Vis (ECCV) 8694:172–187

    Google Scholar 

  17. Isard M, Blake A (1998) CONDENSATION – conditional density propagation for visual tracking. Int J Comput Vis (IJCV) 29(1):5–28

    Article  Google Scholar 

  18. Kalal Z, Matas J, Mikolajczyk K (2010) P-N learning: bootstrapping binary classifiers by structural constraints. IEEE Conf Comput Vis Pattern Recognit (CVPR) 238(6):49–56

    Google Scholar 

  19. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422

    Article  Google Scholar 

  20. Kwak S, Nam W, Han B, Han J H (2011) Learning occlusion with likelihoods for visual tracking. IEEE Int’l Conf Comput Vis (ICCV) 1551–1558

  21. Kwon J, Lee K M (2011) Tracking by sampling trackers. IEEE Int’l Conf Comput Vis (ICCV) 1195–1202

  22. Lee DY, Sim JY, Kim CS (2014) Visual tracking using pertinent patch selection and masking. IEEE Conf Comput Vis Pattern Recognit (CVPR) 3486–3493

  23. Lewis J P (1995) Fast template matching. Vision Interface 120–123

  24. Li X, Hu W, Zhang Z, Zhang X, Zhu M, Cheng J (2008) Visual tracking via incremental log -euclidean riemannian subspace learning. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1–8

  25. Liu B, Huang J, Yang L, Kulikowsk C (2011) Robust tracking using local sparse appearance model and k-selection. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1313–1320

  26. Lucey S (2008) Enforcing non-positive weights for stable support vector tracking. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1–8

  27. Ma C, Yang X, Zhang Cg, Yang MH (2015) Long-term correlation tracking. IEEE Conf Comput Vis Pattern Recognit (CVPR) 5388–5396

  28. Mahadevan V, Vasconcelos N (2013) Biologically inspired object tracking using center-surround saliency mechanisms. IEEE Trans Pattern Anal Mach Intell 35(3):541–554

    Article  Google Scholar 

  29. Mei X, Ling H (2009) Robust visual tracking using L1 minimization. IEEE Int’l Conf Comput Vis (ICCV) 1436–1443

  30. Ozuysal M, Fua P, Lepetit V (2007) Fast keypoint recognition in ten lines of code. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1–8

  31. Ozuysal M, Lepetit V, Fleuret F, Fua P (2006) Feature harvesting for tracking-by-detection. Eur Conf Comput Vis (ECCV) 592–605

  32. Pernici F (2012) Facehugger: the ALIEN tracker applied to faces. Eur Conf Comput Vis (ECCV) 7585:597–601

    Google Scholar 

  33. Piccardi M (2004) Background subtraction techniques: a review. In Proc IEEE Conf Syst Man Cybern 3099–3104

  34. Ross D, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vis (IJCV) 77(1):125–141

    Article  Google Scholar 

  35. Saffari A, Leistner C, Santner J, Godec M, Bischof H (2009) On-line random forests. IEEE Int’l Conf Comput Vis (ICCV) WS On-line Learn Comput Vis 1393–1400

  36. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 14:4–21

    Article  Google Scholar 

  37. Supancic J S, Ramanan D (2013) Self-paced learning for long-term tracking. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2379–2386

  38. Wang X, Hua G, Han T X (2010) Discriminative tracking by metric learning. Eur Conf Comput Vis (ECCV) 200–214

  39. Wu Y, Lim J, Yang M-H (2013) Online object tracking: a benchmark. IEEE Conf Comput Vis Pattern Recognit (CVPR) 2411–2418

  40. Xing J, Gao J, Li B, Hu W, Yan S (2013) Robust object tracking with online multi-lifespan dictionary learning. IEEE Int’l Conf Comput Vis (ICCV) 665–672

  41. Xu Y, Dong J, Zhang B, Xu D (2016) Background modeling methods in video analysis: a review and comparative evaluation. CAAI Trans Intell Tech 1(1):43–60

    Article  Google Scholar 

  42. Yan C, Xu YJ, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  43. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circ Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  44. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv (CSUR) 38(4):13

    Article  Google Scholar 

  45. Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization. Eur Conf Comput Vis (ECCV) 8694:188–203

    Google Scholar 

  46. Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. Eur Conf Comput Vis (ECCV) 864–877

  47. Zuo Z, Wang G, Shuai B, Zhao L, Yang Q (2015) Exemplar based deep discriminative and shareable feature learning for scene image classification. Pattern Recogn 48(10):3004–3015

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Science and Technology Project of Sichuan Province of China (Grant No. 2015JY0141) and the Fundamental Research Funds for the Central Universities of China (Grant No. 2682014cx024).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Quan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quan, W., Liu, Z., Chen, J.X. et al. Adaptive relay detection using primary and auxiliary detectors for tracking. Multimed Tools Appl 76, 24299–24313 (2017). https://doi.org/10.1007/s11042-016-4147-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4147-y

Keywords

Navigation