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CN 34-1054/N

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A simplified non-maximum suppression with improved constraints

  • Department of Automation, University of Science and Technology of China, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2016.01.002
  • Received Date: 14 May 2015
  • Accepted Date: 05 November 2015
  • Rev Recd Date: 05 November 2015
  • Publish Date: 30 January 2016
    Abstract

  • Abstract

    Post processing plays an important role in object detection methods based on the sliding window method. Simplified non-maximum suppression is a typical representative of post processing methods. However, traditional simplified non-maximum suppression uses only one constraint and cannot discard repetitive detections effectively. An improved simplified non-maximum suppression with two additional constraints was proposed. Compared with the traditional simplified non-maximum suppression which only calculates the proportion of intersection area to that of candidate detection bounding box, the two additional constraints named "completely covered detection suppression" and "PASCAL VOC overlap criterion" calculate the proportions of the intersection area to that of the selected detection bounding box and to the union area, respectively. The experimental results show that the improved simplified non-maximum suppression could discard the false positives effectively and significantly improve detection performance.

    Abstract

    Post processing plays an important role in object detection methods based on the sliding window method. Simplified non-maximum suppression is a typical representative of post processing methods. However, traditional simplified non-maximum suppression uses only one constraint and cannot discard repetitive detections effectively. An improved simplified non-maximum suppression with two additional constraints was proposed. Compared with the traditional simplified non-maximum suppression which only calculates the proportion of intersection area to that of candidate detection bounding box, the two additional constraints named "completely covered detection suppression" and "PASCAL VOC overlap criterion" calculate the proportions of the intersection area to that of the selected detection bounding box and to the union area, respectively. The experimental results show that the improved simplified non-maximum suppression could discard the false positives effectively and significantly improve detection performance.
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    • References(11)
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    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.
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    [1]
    郭明玮, 赵宇宙, 项俊平, 等. 基于支持向量机的目标检测算法综述[J]. 控制与决策, 2014, 29(2): 193-200.
    Guo Mingwei, Zhao Yuzhou, Xiang Junping, et al. Review of object detection methods based on SVM[J]. Control and Decision, 2014, 29(2): 193-200.
    [2]
    苏松志, 李绍滋, 陈淑媛, 等. 行人检测技术综述[J]. 电子学报, 2012, 40(4): 814-820.
    SuSongzhi, Li Shaozi, Chen Shuyuan, et al. A survey on pedestrian detection[J]. Acta Electronica Sinca, 2012, 40(4): 814-820.
    [3]
    Viola P, Jones M J. Robust real-time object detection[J]. International Journal of Computer Vision, 2001, 4: 34-47.
    [4]
    Neubeck A, Van Gool L. Efficient non-maximum suppression[C]// Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE Press, 2006, 3: 850-855.
    [5]
    Wu J, Geyer C, Rehg J M. Real-time human detection using contour cues[C]// IEEE International Conference on Robotics and Automation. Shanghai，China: IEEE Press, 2011, 47(10): 860-867.
    [6]
    Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego，USA: IEEE Press, 2005, 1(12): 886-893.
    [7]
    Dollár P, Tu Z, Perona P, et al. Integral channel features[C]// Proceedings of the British Machine Vision Conference. London，UK: BMVC Press，2009: 91(1-11).
    [8]
    Enzweiler M, Gavrila D M. Monocular pedestrian detection: Survey and experiments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2179-2195.
    [9]
    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.
    [10]
    Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes(VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.
    [11]
    Dollár P, Wojek C, Schiele B, et al. Pedestrian detection: A benchmark[C]// IEEE Conference on Computer Vision and Pattern Recognition. Miami，USA: IEEE Press, 2009: 304-311.)
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