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兵工学报 ›› 2016, Vol. 37 ›› Issue (4): 705-711.doi: 10.3969/j.issn.1000-1093.2016.04.019

• 论文 • 上一篇    下一篇

基于分层模型和鲁棒字典学习的背景差分炸点检测

计宏磊1, 杨清文1, 秦晓燕2   

  1. (1.陆军军官学院 远程火箭炮系, 安徽 合肥 230031; 2.陆军军官学院 管理工程系, 安徽 合肥 230031)
  • 收稿日期:2015-07-03 修回日期:2015-07-03 上线日期:2016-06-20
  • 作者简介:计宏磊(1986—)男硕士研究生
  • 基金资助:
    安徽省自然科学基金项目(1508085QF114)

Detection of Blast Point Based on Hierarchical Model of Background Subtraction via Robust Dictionary Learning

JI Hong-lei1, YANG Qing-wen1, QIN Xiao-yan2   

  1. (1.Department of Long-range Rocket Gun, Army Officer Academy of PLA, Hefei 230031, Anhui, China;2.Department of Management Engineering, Army Officer Academy of PLA, Hefei 230031, Anhui, China)
  • Received:2015-07-03 Revised:2015-07-03 Online:2016-06-20

摘要: 针对背景差分炸点检测方法中背景模型难以更新背景估计和运算复杂等问题,提出一种分 层模型下基于鲁棒字典学习的背景差分炸点检测方法。为提高运算效率,该方法对图像帧建立3层金字塔分层模型,在每层将图像帧分割为互不重叠块,逐层以图像块为单位通过改进的鲁棒字典学习方法进行背景估计,与当前图像帧作背景差分实现炸点检测。采用炮弹炸点图像序列对所提出的方法进行了实验验证。实验结果表明,与现有炮弹炸点检测方法相比,该方法在准确率、误检率和鲁棒性方面均具有优越性能。

关键词: 兵器科学与技术, 炸点检测, 背景差分, 分层模型, 鲁棒字典学习

Abstract: For the background estimation and huge computation problems of background model in the background subtraction blast point detection method, a blast point detection method is proposed based on a hierarchical model of background subtraction via robust dictionary learning. To improve the operation efficiency, a three-tier pyramid hierarchical model is established to divide each frame image into non-overlapping blocks. The blast points are detected from the subtraction between current frame image and image background estimation by using the improved robust dictionary learning method layer by layer. Experimental results on a large number of blast point image sequences show that the proposed method has superior performance in correct detection rate, false positive rate and robustness in comparison with the existing blast point detection method.

Key words: ordnance science and technology, blast point detection, background subtraction, hierarchical model, robust dictionary learning

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