Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Evaluation of Tracking Performance: Focusing on Improvement of Aiming Ability for Individual Weapon

개인화기 조준 능력 향상 관점에서의 추적 기법의 성능평가

  • Received : 2013.01.28
  • Accepted : 2013.05.06
  • Published : 2013.05.30
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Abstract

In this paper, an investigation of weapon tracking performance is shown in regard to improving individual weapon performance of aiming objects. On the battlefield, a battle can last only a few hours, sometimes it can last several days until finished. In these long-lasting combats, a wide variety of factors will gradually lower the visual ability of soldiers. The experiments were focusing on enhancing the degraded aiming performance by applying visual tracking technology to roof mounted sights so as to track the movement of troops automatically. In order to select the optimal algorithm among the latest visual tracking techniques, performance of each algorithm was evaluated using the real combat images with characteristics of overlapping problems, camera's mobility, size changes, low contrast images, and illumination changes. The results show that VTD (Visual Tracking Decomposition)[2], IVT (Incremental learning for robust Visual Tracking)[7], and MIL (Multiple Instance Learning)[1] perform the best at accuracy, response speed, and total performance, respectively. The evaluation suggests that the roof mounted sights equipped with visual tracking technology are likely to improve the reduced aiming ability of forces.

본 논문에서는 실제 전장에서 전투수행 중인 병사의 개인화기 조준 능력 향상 관점에서 추적기법의 성능평가를 하였다. 실제 전장에서는 짧은 시간동안 전투를 하는 것뿐만 아니라 며칠에 걸쳐서 실시되는 경우도 있다. 이와 같이 장시간 지속되는 작전 중에서 다양한 요소에 의해서 지속적으로 병사의 신체능력이 감소된다. 이렇게 손실되는 신체능력을 보완하기 위하여 시각추적 기술을 화기의 조준경에 적용하여 적 병사 이동상황을 자동적으로 추적하고 이로 인해 감소된 조준능력을 향상시키기 위한 실험을 하였다. 최신영상 추적 기법들 중에서 최적의 것을 결정하기 위하여, 겹침 현상, 카메라 이동, 크기변화, 저대비 영상, 조명변화 등의 특징이 포함된 여러 실제 전장 영상으로 그 성능을 평가하였다. VTD (Visual Tracking Decomposition)[2]가 정확도에서 IVT (Incremental learning for robust Visual Tracking)[7]가 속도 평가에서 가장 우수하였으며 종합적으로는 MIL (Multiple Instance Learning)[1]이 가장 우수한 결과를 보여 주었다. 이러한 성능평가 결과는 시각추적기술이 적용된 조준경이 실제 전장에서 전투수행을 하면서 신체능력이 감소된 병사의 전투력을 보완할 가능성이 있다는 것을 보여 준다.

Keywords

References

  1. B. Babenko, M. Yang, and S. Belongie. Visual tracking with online multiple instance learning. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2009.
  2. J. Kwon and K. Lee, "Visual tracking decomposition," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269-1276, 2010.
  3. Junseok Kwon and Kyoung Mu Lee. Tracking by sampling trackers. Proceedings of International Conference on Computer Vision, pages 1195-1202, 2011.
  4. Tianzhu Zhang, Bernard Ghanem, Si Liu, Narendra Ahuja. Robust visual tracking via multi-task sparse learning. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2012
  5. U.S. Army. FM 22-9 Soldier Performance In Continuous Operations. 1991
  6. Defense Daily. "Vietnam Var and the advent of M16 Rifle" 2008. 9. 9. in Korean
  7. D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. IJCV, 77(1): 125-141, May 2008. https://doi.org/10.1007/s11263-007-0075-7
  8. Weapons data center of Defense Acquisition Program Administration. in Korean
  9. http://www.cs.toronto.edu/-dross/ivt/
  10. http://vision.ucsd.edu/-bbabenko/data/
  11. http://cv.snu.ac.kr/research/-vtd/
  12. G. H. Golub and C.F. Van Loan. Matrix Computations. The Jojns Hopkins University Press, 1996.
  13. P. Hall, D. Marshall, and R. Martin. Incremental eigenanalysis for classification. Proceedings of British Machine Vision Conference, pages 286-295, 1998.
  14. A. Levy and M. Lindenbaum. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing, 9(8):1371-1374, 2000. https://doi.org/10.1109/83.855432
  15. M. Brand. Incremental singular value decomposition of uncertain data with missing values. In A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, editors, Proceedings of the Seventh European Conference on Computer Vision, LNCS 2350 pages 707-720. Springer Verlag, 2002.
  16. P. Hall, D. Marshall, and R. Martin. Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image and Vision Computing, 20(13-14): 1009-1016, 2002. https://doi.org/10.1016/S0262-8856(02)00114-2
  17. J. Lim, D. Ross, R.-S. Lin, and M.-H. Yang. Incremental learning for visual tracking. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems, pages 793-800. MIT Press, 2005.
  18. R.-S. Lin, D. Ross, J. Lin, and M.-H. Yang. Adaptive discriminative generative model and its applications. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems, pages 801-808. MIT Press, 2005.
  19. M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density. In B. Buxton and R. Cipolla, editors, Proceedings of the Fourth European Conference on Computer Vision, LNCS 1064, pages 343-356. Springer Verlag, 1996.
  20. B. North and A. Blake. Learning dynamical models using expectation- maximization. Proceedings of IEEE International Conference on Computer Vision, pages 384-389, 1998.
  21. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3): 611-622, 1999. https://doi.org/10.1111/1467-9868.00196
  22. S. Roweis. EM algorithms for PCA and SPCA. In M. I. Jordan, M. J. Kearms, and S. A. Solla, editors, Advances in Neural Information Processing Systems 10, pages 626-632. MIT Press, 1997.
  23. M. J. Black and A. D. Jepson. Eigentracking: Robust matching and tracking of articulated objects using view-based representation. In B. Buxton and R. Cipolla, editors, Proceedings of the Fourth European Conference on Computer Vision, LNCS 1064, pages 329-342. Springer Verlag, 1996.
  24. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of IEEE International Conference on Computer Vision, volume 1, pages 511-518, 2001.
  25. P. Dllar, Z. Tu, H. Tao, and S. Belongie. Feature mining for image classification. Proceedings of IEEE International Conference on Computer Vision, June 2007.
  26. J. Wang, X. Chen, and W. Gao. Online selecting discriminative tracking features using particle filter. Proceedings of IEEE International Conference on Computer Vision, volume 2, pages 1037-1042, 2005.
  27. T. G. Dietterich, R. H. Lathrop, and L. T. Perez. Solving the multiple instance problem with axis parallel rectangles. Artificial Intelligence, pages 31-71, 1997.
  28. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. Proceedings of Annual Conference on Neural Information Processing Systems, pages 577-584, 2003.
  29. P. Viola, J. C. Platt, and C. Zhang. Multiple instance boosting for object detection. Proceedings of Annual Conference on Neural Information Processing Systems, pages 1417-1426, 2005.
  30. J. Friedman, Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232, 2001.
  31. N. C. Oza. Online Ensemble Learning. Ph.D. Thesis, University of California, Berkeley, 2001.
  32. H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via online boosting. Proceedings of British Machine Vision Conference, pages 47-56, 2006.
  33. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 28(2): 337-407, 2000.
  34. H. Ling and K. Okade. Diffusion distance for histogram comparison. Proceedings of IEEE International Conference on Computer Vision, 2006
  35. A. d'Aspremont, L. El Ghaoui, M. Jordan, and G. Lanckriet. A direct formulation for sparse PCA using semidefinite programming. SIAM Review, 49(3). 2007.
  36. J. Corander, M Ekdahl, and T. Koski. Parallell interacting MCMC for learning of topologies of graphical models. Data Min. Knowl. Discov., 17(3), 2007