International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
권호 표지
DOI QR코드

DOI QR Code

POSE-VIWEPOINT ADAPTIVE OBJECT TRACKING VIA ONLINE LEARNING APPROACH

  • Mariappan, Vinayagam (Media IT Engineering, Seoul National Univ., of Science and Tech.) ;
  • Kim, Hyung-O (Graduate School of NID Fusion Tech., Seoul National Univ., of Science and Tech.) ;
  • Lee, Minwoo (Graduate School of NID Fusion Tech., Seoul National Univ., of Science and Tech.) ;
  • Cho, Juphil (Dept. Of Integrated IT & Communication Eng., Kunsan National Univ.) ;
  • Cha, Jaesang (Graduate School of NID Fusion Tech., Seoul National Univ., of Science and Tech.)
  • Received : 2015.09.05
  • Accepted : 2015.10.15
  • Published : 2015.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

Keywords

References

  1. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark," In CVPR, pp. 2411-2418, 2013.
  2. A. Smeulders, D. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: An experimental survey," TPAMI, Vol. 36, No. 7, pp.1442-1468, 2014. https://doi.org/10.1109/TPAMI.2013.230
  3. M. Kristan and R. Pflugfelder et al. "The visual object tracking VOT2014 challenge results," In ECCV Workshop, pp.1-27, 2014.
  4. H. Yang, L. Shao, F. Zheng, L. Wang, and Z. Song, "Recent advances and trends in visual tracking: A review," Neurocomputing, Vol. 74, No. 18, pp. 3823-3831, 2011. https://doi.org/10.1016/j.neucom.2011.07.024
  5. X. Li,W. Hu, C. Shen, Z. Zhang, A. Dick, and A. V. D. Hengel, "A survey of appearance models in visual object tracking," TIST, Vol. 4, No. 4, pp. 58, 2013.
  6. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental learning for robust visual tracking," IJCV, Vol. 77(1-3), pp. 125-141, 2008. https://doi.org/10.1007/s11263-007-0075-7
  7. X. Mei and H. Ling, "Robust visual tracking and vehicle classification via sparse representation," TPAMI, Vol. 33, No. 11, pp. 2259- 2272, 2011. https://doi.org/10.1109/TPAMI.2011.66
  8. Z. Hong, X. Mei, D. Prokhorov, and D. Tao, "Tracking via robust multi-task multi-view joint sparse representation," In ICCV, pp. 649-656, 2013.
  9. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, "Robust visual tracking via multi-task sparse learning," In CVPR, pp. 2042-2049, 2012.
  10. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental learning for robust visual tracking," IJCV, Vol. 77(1-3), pp. 125-141, 2008. https://doi.org/10.1007/s11263-007-0075-7
  11. G. Nebehay and R. Pflugfelder, "Consensus-based matching and tracking of keypoints for object tracking," In WACV, pp. 862-869, 2014.
  12. S. Avidan, "Support vector tracking," TPAMI, Vol. 26(8), pp. 1064-1072, 2004. https://doi.org/10.1109/TPAMI.2004.53
  13. S. Avidan, "Ensemble tracking," TPAMI, Vol. 29(2), pp. 261-271, 2007. https://doi.org/10.1109/TPAMI.2007.35
  14. B. Babenko, M.-H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning," TPAMI, Vol. 33(8), pp. 1619-1632, 2011. https://doi.org/10.1109/TPAMI.2010.226
  15. S. Hare, A. Saffari, and P. H. Torr, "Struck: Structured output tracking with kernels," In ICCV, pp. 263-270, 2011.
  16. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," In ECCV, pp. 702-715. 2012.
  17. Z. Hong, X. Mei, and D. Tao, "Dual-force metric learning for robust distracter-resistant tracker," In ECCV, pp. 513-527, 2012.
  18. Y. Pang and H. Ling, "Finding the best from the second bestsinhibiting subjective bias in evaluation of visual tracking algorithms," In ICCV, pp. 2784-2791, 2013.
  19. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark," In CVPR, pp. 2411-2418, 2013.
  20. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, "Visual object tracking using adaptive correlation filters," In CVPR, pp. 2544-2550, 2010.
  21. M. Danelljan, G. Hager, F. S. Khan, and M. Felsberg, "Accurate scale estimation for robust visual tracking," In BMVC, 2014.
  22. M. Danelljan, F. Shahbaz Khan, M. Felsberg, and J. Van de Weijer, "Adaptive color attributes for real-time visual tracking," In CVPR, pp. 1090-1097, 2014.
  23. J. Henriques, R. Caseiro, P. Martins, and J. Batista, "Highspeed tracking with kernelized correlation filters," TPAMI, pp. 583-596, 2015.
  24. Y. Li and J. Zhu, "A scale adaptive kernel correlation filter tracker with feature integration," In ECCV Workshop, 2014.
  25. J. Wu, G. Li, and F. Ma, "Research on target tracking algorithm using improved current statistical model," International Conference on Electrical and Control Engineering, pp. 2515-2517, 2011.
  26. C. Huang, P. Feng, L. Cao, H. Huang, and H. Cheng, "A target tracking algorithm based on current statistical model for adjusting acceleration variance of maneuver target," Journal of Northwestern Polytechnical University, 2014.
  27. Q. Li, L. Kong, and X. Yang, "The knowledge-based tracking using geographic information," IEEE Conference on Radar, pp. 777-780, 2011.
  28. A. Mazinan, A. Amir, and M. Kazemi, "A knowledge-based objects tracking algorithm in color video using Kalman filter approach," IEEE Conference on Information Retrieval and Knowledge Management, 2012.
  29. I. Barkana, "On adaptive model tracking with mitigated passivity conditions," Israel Annual Conference on Aerospace Sciences, pp. 512-541, 2012.
  30. J. Hou, X Li, and Z. Jing, "Multiple model tracking of manoeuvring targets accounting for standoff jamming information," IET Radar, Sonar and Navigation, Vol. 7, No. 4, pp. 342-350, 2013. https://doi.org/10.1049/iet-rsn.2012.0279
  31. W. Kazimierski and A. Stateczny, "Optimization of multiple model neural tracking filter for marine targets," International Radar Symposium, pp. 543-548, 2012.
  32. H. Wang, B. Chen, X. Liu, K. Liu, and C. Lin, "Adaptive neural tracking control for stochastic nonlinear strict-feedback systems with unknown input saturation," Information Sciences, Vol. 269, pp. 300-315, 2014. https://doi.org/10.1016/j.ins.2013.09.043
  33. S. Avidan, "Ensemble tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2), pp. 261-271, Feb. 2007. https://doi.org/10.1109/TPAMI.2007.35
  34. B. Babenko, Ming-Hsuan Yang, and S. Belongie, "Visual tracking with online multiple instance learning," In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), pp. 983-990, IEEE, June 2009.
  35. R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley Publishing, 2009.
  36. Z. Kalal, J. Matas, and K. Mikolajczyk, "Online learning of robust object detectors during unstable tracking," In Proceedings of the IEEE On-line Learning for Computer Vision Workshop, pp. 1417-1424, 2009.
  37. Z. Kalal, J. Matas, and K. Mikolajczyk, "P-N learning: Bootstrapping binary classifiers by structural constraints," In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49-56, IEEE, June 2010.
  38. Z. Kalal, K. Mikolajczyk, and J. Matas, "Forward-Backward Error: Automatic Detection of Tracking Failures," In International Conference on Pattern Recognition, pp. 23-26, 2010.