Vol. 130
Latest Volume
All Volumes
PIER 185 [2026] PIER 184 [2025] PIER 183 [2025] PIER 182 [2025] PIER 181 [2024] PIER 180 [2024] PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2012-08-21
Image Sequence Measures for Automatic Target Tracking
By
Progress In Electromagnetics Research, Vol. 130, 447-472, 2012
Abstract
In the field of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking difficulties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the field of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.
Citation
Wei-He Diao, Xia Mao, Hai-Chao Zheng, Yu-Li Xue, and Vasile Gui, "Image Sequence Measures for Automatic Target Tracking," Progress In Electromagnetics Research, Vol. 130, 447-472, 2012.
doi:10.2528/PIER12050810
References

1. Vicen-Bueno, R., M. Rosa-Zurera, M. P. Jarabo-Amores, et al. "Automatic target detection in simulated ground clutter (Weibull distributed) by multilayer perceptrons in a low-resolution coherent radar," IET Radar, Sonar and Navigation, Vol. 4, No. 2, 315-328, 2010.
doi:10.1049/iet-rsn.2009.0080        Google Scholar

2. Mušicki, D., "Doppler-aided target tracking in heavy clutter," Proceedings of the International Conference on Information Fusion , 1-7, 2010.        Google Scholar

3. Dudgeon, D. E. and R. T. Lacoss, "An overview of automatic target recognition," Lincoln Laboratory Journal, Vol. 6, No. 1, 3-9, 1993.        Google Scholar

4. Fiala, P., T. Jirku, R. Kubásek, P. Drexler, and P. Koňas, "A passive optical location with limited range," PIERS Online, Vol. 2, No. 6, 685-688, 2006.
doi:10.2529/PIERS060901095834        Google Scholar

5. Tian, B., D.-Y. Zhu, and Z.-D. Zhu, "A novel moving target detection approach for dual-channel SAR system," Progress In Electromagnetics Research, Vol. 115, 191-206, 2011.        Google Scholar

6. AlShehri, S. A., S. Khatun, A. B. Jantan, R. S. A. Raja Abdullah, R. Mahmood, and Z. Awang, "Experimental breast tumor detection using NN-based UWB imaging," Progress In Electromagnetics Research, Vol. 111, 447-465, 2011.
doi:10.2528/PIER10110102        Google Scholar

7. Huang, C.-W. and K.-C. Lee, "Application of ICA technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 105, 157-170, 2010.
doi:10.2528/PIER10042305        Google Scholar

8. Chang, Y.-L., C.-Y. Chiang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 119, 35-57, 2011.
doi:10.2528/PIER11061507        Google Scholar

9. Wang, X., J.-F. Chen, Z.-G. Shi, and K. S. Chen, "Fuzzy-control-based particle filter for maneuvering target tracking," Progress In Electromagnetics Research, Vol. 118, 1-15, 2011.
doi:10.2528/PIER11051907        Google Scholar

10. Wang, Q. C., J. Li, M. Zhang, and C. Yang, "H-infinity filter based particle filter for maneuvering target tracking," Progress In Electromagnetics Research B, Vol. 30, 103-116, 2011.        Google Scholar

11. Chen, Y., G. Chen, R. S. Blum, et al. "Image quality measures for predicting automatic target recognition performance," Proceedings of the IEEE Aerospace Conference, 1-8, 2008.

12. Clark, L. G. and V. J. Velten, "Image characterization for automatic target recognition algorithm evaluations," Optical Engineering, Vol. 30, No. 2, 147-153, 1991.
doi:10.1117/12.55784        Google Scholar

13. Liu, R., E. Liu, J. Yang, et al. "Point target detection of infrared images with eigentargets," Optical Engineering, Vol. 46, No. 11, 501-503, 2007.
doi:10.1117/1.2802301        Google Scholar

14. Ma, Y. and B. Kong, "A study of object detection based on fuzzy support vector machine and template matching," Proceedings of the World Congress on Intelligent Control and Automation, 4137-4140, 2004.        Google Scholar

15. Yang, L. and J. Yang, "Detection of small targets with adaptive binarization threshold in infrared video sequences," Chinese Optics Letters, Vol. 4, No. 3, 152-154, 2006.        Google Scholar

16. Abousleman, G. P., M. W. Marcellin, and B. R. Hunt, "Hyperspectral image compression using entropy-constrained predictive trellis coded quantization ," IEEE Transactions on Image Processing, Vol. 6, No. 4, 566-573, 1997.
doi:10.1109/83.563321        Google Scholar

17. Dachasilaruk, S., "Wavelet shrinkage and compression for SAR images," Proceedings of the International Multi-conference on Systems, Signals and Devices, 1-6, 2008.

18. Prasantha, H. S., H. L. Shashidhara, and K. N. Balasubramanya Murthy, "Image compression using SVD," Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, 143-145, 2007.        Google Scholar

19. Terki, N., N. Doghmane, Z. Baarir, et al. "Study of filter effects in wavelet image compression," Proceedings of the International Conference on Information and Communication Technologies: From Theory to Applications , 369-370, 2004.

20. Trieu-Kien, T., J. Jyh-Horng, I. S. Reed, et al. "A fast encoding algorithm for fractal image compression using the DCT inner product," IEEE Transactions on Image Processing, Vol. 9, No. 4, 529-535, 2000.
doi:10.1109/83.841930        Google Scholar

21. Wang, Z., L. Lu, and A. C. Bovik, "Video quality assessment based on structural distortion measurement," Image Communication, Vol. 19, No. 2, 121-132, 2004.        Google Scholar

22. Richard, A. P. I. and N. S. Robin, "Image complexity metrics for automatic target recognizers," Proceedings of the Automatic Target Recognizer System and Technology Conference, 1-17, 1990.        Google Scholar

23. Beard, J., L. Clark, and V. Velten, "Characterization of ATR performance in relation to image measurements," ATRWG Report, AFWAL/AARF, Wright Patterson AFB, OH, 1985.        Google Scholar

24. Gao, S. and P.-L. Shui, "Method for moving point target detection in image sequences based on directional cumulation," Proceedings of the SPIE 6795, 67952I-1-67952I-6, 2007.        Google Scholar

25. Nevis, A., "Image characterization and target recognition the surf zone environment," Proceedings of the SPIE, Vol. 2765, 46-58, 1996.
doi:10.1117/12.241263        Google Scholar

26. Yonoviz, D., "Tunable wavelet target extraction preprocessor," Proceedings of the SPIE, Vol. 6569, 1-12, 2007.        Google Scholar

27. Mao, X. and W.-H. Diao, "Criterion to evaluate the quality of infrared small target images," Journal of Infrared, Millimeter and Terahertz Waves, Vol. 30, No. 1, 56-64, 2009.
doi:10.1007/s10762-008-9410-5        Google Scholar

28. Lahart, M., T. Jones, and F. Shields, "Trends and capabilities of ATR algorithm technology for ground vehicles," Proceedings of the IRIS Conference on Targets, Backgrounds, and Discrimination, Monterey, CA, 1988.        Google Scholar

29. Sadjadi, F., "Measures of effectiveness and their use in comparative image fusion analysis," Proceedings of the IEEE Geoscience and Remote Sensing Symposium, 3659-3661, 2003.

30. Garlson, J. J., J. B. Jordan, and G. M. Flachs, "Task specific complexity metrics for electronic vision," Proceedings of the International Conference on Image Processing, Analysis, Measurement, and Quality, 35-40, 1988.        Google Scholar

31. Sadjadi, F. A. and M. E. Bazakos, "Perspective on automatic target recognition evaluation technology," Optical Engineering, Vol. 30, No. 2, 183-188, 1991.
doi:10.1117/12.55788        Google Scholar

32. Loyd, G. C. and J. V. Vincent, "Image characterization for automatic target recognition algorithm evaluations," Optical Engineering, Vol. 30, No. 2, 147-153, 1990.        Google Scholar

33. Todt, E. and C. Torras, "Detection of natural landmarks through multiscale opponent features," Proceedings of the International Conference on Pattern Recognition, 976-979, 2000.
doi:10.1109/ICPR.2000.903708        Google Scholar

34. Zhou, C., G. Zhang, and J. Pen, "A general evaluation method for segmentation algorithm based on experimental design methodology," Proceedings of the IEEE International Conference on Systems, Man and Cybernetics , 258-262, 1995.        Google Scholar

35. Vicen-Bueno, R., R. Carrasco-Alvarez, M. Rosa-Zurera, et al. "Sea clutter reduction and target enhancement by neural networks in a marine radar system ," Sensors, Vol. 9, 1913-1936, 2009.
doi:10.3390/s90301913        Google Scholar

36. Bhanu, B., "Automatic target recognition: State of the art survey," IEEE Transactions on Aerospace and Electronic Systems, Vol. 22, No. 4, 364-379, 1986.
doi:10.1109/TAES.1986.310772        Google Scholar

37. Schmieder, D. E. and M. R. Weathersby, "Detection performance in clutter with variable resolution," IEEE Transactions on Aerospace and Electronic Systems, Vol. 19, No. 4, 622-630, 1983.
doi:10.1109/TAES.1983.309351        Google Scholar

38. Rotman, S. and M. L. Kowalczyk, "Clutter analysis for modeling and improving human and automatic target acquisition," Proceedings of the SPIE 2020, 131-142, 1993.
doi:10.1117/12.160534        Google Scholar

39. Tidhar, G., G. Reiter, Z. Avital, et al. "Modeling human search and target acquisition performance: IV. Detection probability in the cluttered environment," Optical Engineering, Vol. 33, No. 3, 801-808, 1994.
doi:10.1117/12.160980        Google Scholar

41. Young, R. A., "Simulation of human retinal function with the Gaussian derivative model," Proceedings of the IEEE Proceedings Proceedings of the IEEE Proceedings, 564-569, 1986.        Google Scholar

42. Meitzler, T. J., R. E. Karlsen, G. R. Gerhart, et al. "Wavelet transforms of cluttered images and their application to computing the probability of detection," Optical Engineering, Vol. 35, No. 10, 3019-3025, 1996.
doi:10.1117/1.600987        Google Scholar

43. Haralick, R. M., K. Shanmugan, and I. Dinstein, "Texture features for image classification," IEEE Transactions on System, Man and Cybernetics, 610-621, 1973.
doi:10.1109/TSMC.1973.4309314        Google Scholar

44. Waldman, G., J. Wootton, G. Hobson, et al. "A normalized clutter measure for image," Computer Vision, Graphics and Image Processing, Vol. 42, No. 3, 137-156, 1988.
doi:10.1016/0734-189X(88)90161-2        Google Scholar

45. Aviram, G. and S. R. Rotman, "Evaluation of human detection performance of targets and false alarm, using a statistical texture image metrics," Optical Engineering, Vol. 39, No. 8, 2285-2295, 2000.
doi:10.1117/1.1304925        Google Scholar

46. Trievdi, M. M. and M. V. Schirvaikar, "Quantitative characterization of image clutter: Problems, progress, and promises," Proceedings of the International Conference on Characterization, Propagation, and Simulation of Sources and Backgrounds, 288-299, 1993.        Google Scholar

47. Conners, R. and C. Harlow, "A theoretical comparison of texture algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2, No. 2, 204-222, 1980.
doi:10.1109/TPAMI.1980.4767008        Google Scholar

48. Li, M. and G. Zhang, "Image measures for segmentation algorithm evaluation of automatic target recognition system," Proceedings of the International Symposium on Systems and Control in Aerospace and Astronautics, 673-679, 2006.

49. He, G., J. Zhang, and H. Chang, "Clutter metric based on the Cramer-Rao lower bound on automatic target recognition," Applied Optics, Vol. 47, No. 29, 5534-5540, 2008.
doi:10.1364/AO.47.005534        Google Scholar

50. Chang, H.-H. and J.-Q. Zhang, "Evaluation of human detection performance using target structure similarity clutter metrics," Optical Engineering, Vol. 45, No. 9, 41-47, 2006.        Google Scholar

51. Chang, H. and J. Zhang, "New metrics for clutter affecting human target acquisition," IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 1, 361-368, 2006.
doi:10.1109/TAES.2006.1603429        Google Scholar

52. Wu, B., H.-B. Ji, and P. Li, "New method for moving dim target detection based on third-order cumulant in infrared image," Journal of Infrared and Millimeter Waves, Vol. 25, No. 5, 364-367, 2006.        Google Scholar

53. Aviram, G. and S. R. Rotam, "Analyzing the effect of imagery wavelength on the agreement between various image metrics and human detection performance of targets embedded in natural images ," Optical Engineering, Vol. 40, No. 9, 1877-1884, 2001.
doi:10.1117/1.1390296        Google Scholar

54. Rotman, S. R., D. Hsu, A. Cohen, et al. "Textural metrics for clutter affecting human target acquisition," Infrared Physics & Technology, Vol. 37, No. 6, 667-674, 1996.
doi:10.1016/1350-4495(95)00132-8        Google Scholar

55. Yang, L., Study on infrared small target detection and tracking algorithm under complex backgrounds , Ph.D. Thesis, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 2006.