Grid-like radar detection based on the distribution of key points

doi:10.3969/j.issn.0253-2778.2015.10.009

Abstract

Abstract: Grid-like radars have been widely used for military applications, and their detection is of great importance. A novel method is proposed to detect grid-like radars even with large appearance variation. In our method, the key points of grid-like radars are first treated as small objects and detected by the classical sliding window method. Then a possible radar area is located based on the distribution density of the detected key points. Finally the decision regarding the presence/absence of grid-like radars will be made based on the spatial distribution relation of the detected key points. Experiments were done on our dataset, including 42 grid-like radar images and 154 non-radar images, and our approach achieved a 7.1% miss rate and 12.3 FPR(false positive rate). The method based on the distributions of key points is more robust against the appearance variation caused by the types of radar, deformations and viewpoint changes, and demonstrates better performance than classical method, such as “BOF+SIFT” and “HOG”.

Key words: computer vision, object recognition, grid-like radar detection, distribution of key points

CLC Number:

TP391.4

DU Binbin, LING Qiang, LI Feng, SUN Tao. Grid-like radar detection based on the distribution of key points[J]. Journal of University of Science and Technology of China, 2015, 45(10): 864-870.

References

［1］
Viola P, Jones M J. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
[2] Ali K, Fleuret F, Hasler D, et al. A real-time deformable detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2): 225-239.
[3] Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.
[4] Felzenszwalb P F, Girshick R B, McAllester D. Cascade object detection with deformable part models[C]// IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE Press, 2010: 2241-2248.
[5] LIF F, Fergus R, Perona P. Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories[J]. Computer Vision and Image Understanding, 2007, 106(1): 59-70.
[6] 贾平, 徐宁, 张叶. 基于局部特征提取的目标自动识别[J]. 光学精密工程, 2013, 21(7): 1898-1905.
Jia P, Xu N, Zhang Y. Automatic target recognition based on local feature extraction[J]. Optics and Precision Engineering, 2013, 21(7): 1898-1905.
[7] Chen Y Y, Hsu W H, Liao H Y M.Discovering informative social subgraphs and predicting pairwise relationships from group photos[C]// Proceedings of the 20th ACM International Conference on Multimedia. Nara, Japan: ACM Press, 2012: 669-678.
[8] Gallagher A C, Chen T. Understanding images of groups of people[C]// IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE Press, 2009: 256-263.
[9] Harris C, Stephens M. A combined corner and edge detector[C]// Proceedings of the 4th Alvey Vision Conference. Manchester, UK: ACM Press, 1988: 147-151.
[10] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of Computer Vision, 2004, 60(2): 91-110.
[11] Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[12] 李平, 魏仲慧, 何昕, 等. 采用多形状特征融合的多视点目标识别[J]. 光学精密工程, 2014, 22(12): 3368-3376.
Li P, Wei Z H, He X, et al. Object recognition based on shape feature fusion under multi-views[J]. Optics and Precision Engineering, 2014, 22(12): 3368-3376.
[13] Forsyth D A, Ponce J. Computer Vision: A Modern Approach [M]. 2ed, New Jersey: Prentice Hall, 2002.
[14] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]// IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE Press, 2005: 886-893.
[15] Chang C C, Lin C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):389-396.
[16] Hough P V C. Method and means for recognizing complex patterns, U.S. Patent, 3069654[P]. 1962.
[17] Van de Sande K E A, Uijlings J R R, Gevers T, et al. Segmentation as selective search for object recognition[C]// IEEE International Conference on Computer Vision. Barcelona, Spanish: IEEE Press, 2011: 1879-1886.

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