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http://dx.doi.org/10.5909/JBE.2013.18.3.481

Evaluation of Tracking Performance: Focusing on Improvement of Aiming Ability for Individual Weapon  

Kim, Sang Hoon (Electrical Eng., Seoul Nat'l University)
Yun, Il Dong (Hankuk University of Foreign Studies)
Publication Information
Journal of Broadcast Engineering / v.18, no.3, 2013 , pp. 481-490 More about this Journal
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.
Keywords
visual tracking algorithm; aiming ability; battlefield of situation; tracking performance evaluation; roof mounted sight;
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1 J. Corander, M Ekdahl, and T. Koski. Parallell interacting MCMC for learning of topologies of graphical models. Data Min. Knowl. Discov., 17(3), 2007
2 Weapons data center of Defense Acquisition Program Administration. in Korean
3 http://www.cs.toronto.edu/-dross/ivt/
4 http://vision.ucsd.edu/-bbabenko/data/
5 http://cv.snu.ac.kr/research/-vtd/
6 G. H. Golub and C.F. Van Loan. Matrix Computations. The Jojns Hopkins University Press, 1996.
7 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.
8 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.
9 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.
10 P. Dllar, Z. Tu, H. Tao, and S. Belongie. Feature mining for image classification. Proceedings of IEEE International Conference on Computer Vision, June 2007.
11 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.
12 J. Kwon and K. Lee, "Visual tracking decomposition," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269-1276, 2010.
13 Junseok Kwon and Kyoung Mu Lee. Tracking by sampling trackers. Proceedings of International Conference on Computer Vision, pages 1195-1202, 2011.
14 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
15 U.S. Army. FM 22-9 Soldier Performance In Continuous Operations. 1991
16 Defense Daily. "Vietnam Var and the advent of M16 Rifle" 2008. 9. 9. in Korean
17 D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. IJCV, 77(1): 125-141, May 2008.   DOI   ScienceOn
18 P. Hall, D. Marshall, and R. Martin. Incremental eigenanalysis for classification. Proceedings of British Machine Vision Conference, pages 286-295, 1998.
19 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.
20 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.   DOI   ScienceOn
21 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.   DOI   ScienceOn
22 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.
23 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.
24 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.
25 B. North and A. Blake. Learning dynamical models using expectation- maximization. Proceedings of IEEE International Conference on Computer Vision, pages 384-389, 1998.
26 M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61(3): 611-622, 1999.   DOI   ScienceOn
27 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.
28 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.
29 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.
30 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.
31 J. Friedman, Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232, 2001.
32 N. C. Oza. Online Ensemble Learning. Ph.D. Thesis, University of California, Berkeley, 2001.
33 H. Grabner, M. Grabner, and H. Bischof. Real-time tracking via online boosting. Proceedings of British Machine Vision Conference, pages 47-56, 2006.
34 J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 28(2): 337-407, 2000.
35 H. Ling and K. Okade. Diffusion distance for histogram comparison. Proceedings of IEEE International Conference on Computer Vision, 2006
36 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.