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.
"Image Sequence Measures for Automatic Target Tracking," Progress In Electromagnetics Research,
Vol. 130, 447-472, 2012. doi:10.2528/PIER12050810
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
2. Mušicki, D., "Doppler-aided target tracking in heavy clutter," Proceedings of the International Conference on Information Fusion , 1-7, 2010.
3. Dudgeon, D. E. and R. T. Lacoss, "An overview of automatic target recognition," Lincoln Laboratory Journal, Vol. 6, No. 1, 3-9, 1993.
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
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.
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
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
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
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
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.
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
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
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.
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.
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
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.
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
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.
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.
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.
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.
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
26. Yonoviz, D., "Tunable wavelet target extraction preprocessor," Proceedings of the SPIE, Vol. 6569, 1-12, 2007.
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
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.
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.
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
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.
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
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.
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
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
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
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
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
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.
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
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
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
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
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.
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
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
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.
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
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.
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
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
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.