ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

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

  • 1.

    University of Science and Technology of China Department of Automation, Hefei Anhui 230022,China

  • 2.

    Opto-electronic Department in Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun Jilin 130033,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.009
  • Received Date: 22 April 2015
  • Accepted Date: 10 June 2016
  • Rev Recd Date: 10 June 2016
  • Publish Date: 30 October 2015
    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”.

    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”.
    • FullText(HTML)
  • loading
    • References(17)
  • [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.)
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [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.)
    Recommended articles
    TrendMD
    Volume 45 Issue 10 page: 864-870
    Cover

    Article Metrics

    Article views (31) PDF downloads(93)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return