Abstract
Detecting and striking the critical part of blurred balloon image is an important approach to counterattack the reconnaissance balloons. The existing algorithms of the image target detection task are not able to achieve the high precision and real-time performance in the meanwhile since the critical part of the balloon is tiny, weak and not easy to be segmented. In this paper, a real-time algorithm based curvature feature in the polar coordinate system is proposed to detect the critical part of reconnaissance balloons. We divide the proposed method into three steps: the image is firstly subjected to a gray-scale projection by calculating third-order post-difference, then the balloon boundary is extracted in transformed polar coordinates, and finally the boundary curvature identifies the position of the critical part. The core strategy of the proposed method is to adopt the boundary features of the balloon instead of the general time-consuming image operations (e.g. region labeling, matching) to capture the target part. The experimental results show that the proposed method obtains high precision results with a real-time detection. Our proposed method achieves a processing speed of 200 frames per second on DSP (TMS320C6678) while a state-of-the-art detection precision (>93\(\%\)), which overcomes the existing comparison algorithms.
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References
Alcantarilla, P.F., Bartoli, A., Davison, A.J.: Kaze features. European Conference on Computer Vision (ECCV). Springer, Berlin (2012)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. Comput. Vis. Pattern Recogn. 2010, 2544–2550 (2010)
Chao, M., Huang, J.B., Yang, X., Yang, M.H.: Adaptive correlation filters with long-term and short-term memory for object tracking. Int. J. Comput. Vis. 126(2), 1–26 (2017)
Chen, C.L.P., Hong, L., Wei, Y., Tian, X., Yuan, Y.T.: A local contrast method for small infrared target detection. IEEE Trans. Geosci. Rem. Sens. 52(1), 574–581 (2013)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 1, 886–893 (2005)
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: Discriminative scale space tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2017)
Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Viosion (ICCV), pp. 1440–1448. Santiago (2015)
Guan, L., Liu, S., Chu, J., Zhang, R., Chen, Y., Li, S., Zhai, L., Li, Y., Xie, H.: A novel algorithm for estimating the relative rotation angle of solar azimuth through single-pixel rings from polar coordinate transformation for imaging polarization navigation sensors. Optik 178, 868–878 (2019)
Hao, Y., Mu, T., Goulermas, J.Y., Jiang, J., Hong, R., Wang, M.: Unsupervised t-distributed video hashing and its deep hashing extension. IEEE Trans. Image Process. 26(11), 5531–5544 (2017)
Hao, Y., Mu, T., Hong, R., Meng, W., Goulermas, J.Y.: Stochastic multiview hashing for large-scale near-duplicate video retrieval. IEEE Trans. Multimedia 19(1), 1–14 (2017)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Huertas, A., Medioni, G.: Detection of intensity changes with subpixel accuracy using laplacian-gaussian masks. IEEE Trans. Pattern Anal. Mach. Intell. 8(5), 651–664 (1986)
Jasani, B.A., Lam, S.K., Meher, P.K., Wu, M.: Threshold-guided design and optimization for harris corner detector architecture. IEEE Trans. Circ. Syst. Video Technol. 28(12), 3516–3526 (2017)
Kanatani, K., Rangarajan, P.: Hyper least squares fitting of circles and ellipses. Comput. Stat. Data Anal. 55(6), 2197–2208 (2011)
Li, X., Qin, S.Y.: Efficient fusion for infrared and visible images based on compressive sensing principle. Iet Image Process. 5(2), 141–147 (2011)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. Eur. Conf. Comput. Vis. 2016, 2137 (2016)
Lowe, D.G., Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Lukežič, A., Vojíř, T., Zajc, L.Č., Matas, J., Kristan, M.: Discriminative correlation filter tracker with channel and spatial reliability. Int. J. Comput. Vis. 126(7), 671–688 (2018)
Possa, P.R., Mahmoudi, S.A., Harb, N., Valderrama, C., Manneback, P.: A multi-resolution fpga-based architecture for real-time edge and corner detection. IEEE Trans. Comput. 63(10), 2376–2388 (2013)
Redmon, J., Farhadi, A.: Yolo9000: Better, faster, stronger. arXiv:1612.08242 (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2015)
Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Handels, H.: Fast explicit diffusion for registration with direction-dependent regularization. Lecture Notes Comput. Sci. 7359, 220–228 (2012)
Tareen, S.A.K., Saleem, Z.: A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp. 1–10 (2018)
Vala, H.J., Baxi, A.: A review on otsu image segmentation algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(2), 387–389 (2013)
Wang, Q., Zhang, X., Chen, G., Fan, D., Gong, Y., Zhu, K.: Change detection based on faster r-cnn for high-resolution remote sensing images. Remote Sens. Lett. 9(10), 923–932 (2018)
Xing, Y., Zhang, D., Zhao, J., Sun, M., Jia, W.: Robust fast corner detector based on filled circle and outer ring mask. Iet Image Process. 10(4), 314–324 (2016)
Zhang, X., Pan, Z., Hu, B., Xi, Z., Liu, W.: Target detection of hyperspectral image based on spectral saliency. IET Image Process. 13(2), 316–322 (2019)
Zhou, D., Zong, J.: Minimum error thresholding based on two dimensional histogram. Wri World Congress Comput. Sci. Inf. Eng. 7, 169–175 (2009)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (no. 61671337). We thank Mr.HuiYuan Chen of Wuhan Institute of Technology for assistance with experiments. Our deepest gratitude goes to the anonymous reviewers and editor for their careful work and thoughtful suggestions that have helped improve this paper substantially.
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Hong, H., Shi, J., Liu, Z. et al. A real-time critical part detection for the blurred image of infrared reconnaissance balloon with boundary curvature feature analysis. J Real-Time Image Proc 18, 619–634 (2021). https://doi.org/10.1007/s11554-020-00997-6
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DOI: https://doi.org/10.1007/s11554-020-00997-6