吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1976-1985.doi: 10.13229/j.cnki.jdxbgxb201706041

• 论文 • 上一篇    下一篇

基于改进粒子滤波的煤矿视频监控模糊目标检测

杨超宇1, 2, 李策1, 梁胤程1, 杨峰1   

  1. 1.中国矿业大学(北京) 机电与信息工程学院,北京 100083;
    2.安徽理工大学 经济与管理学院,安徽 淮南 232001
  • 收稿日期:2016-10-13 出版日期:2017-11-20 发布日期:2017-11-20
  • 作者简介:杨超宇(1981-),男,副教授,博士.研究方向:计算机视觉,图像分析,多媒体物联网.E-mail:yangchy@aust.edu.cn
  • 基金资助:
    国家自然科学基金项目(51404007,61601466); 安徽省重大教学改革研究项目(2015zdjy074)

Blurred object detection based on improved particle filter in coal mine underground surveilance

YANG Chao-yu1, 2, LI Ce1, LIANG Yin-cheng1, YANG Feng1   

  1. 1.School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China;
    2.School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China
  • Received:2016-10-13 Online:2017-11-20 Published:2017-11-20

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摘要： 针对煤矿井下智能视频监控采集到的视频图像质量较差、干扰多、噪点多、目标检测不准确等问题,提出了一种基于改进粒子滤波的模糊目标检测方法。在标准粒子滤波理论框架下,以视频多图像帧差为基础构建非线性、非高斯多系统状态空间融合模型;在图像帧差得到的关键点区域范围内进行粒子抽样及概率密度的传播;利用加权后验样本粒子表示多系统状态空间融合模型的后验概率密度;采用样本均值方法融合估计系统后验状态;最后对系统状态空间模型进行输出,得到目标检测结果。使用三交河煤矿井下视频监控数据进行试验验证,分别釆用改进的粒子滤波、标准粒子滤波、无损卡尔曼滤波以及扩展卡尔曼滤波等方法进行对比。试验结果表明:针对煤矿井下视频模糊目标检测问题,改进的多状态空间模型及关键区域采样粒子滤波算法具有良好的目标检测效果。

关键词: 信息处理技术, 目标检测, 粒子滤波, 多状态空间, 关键区域采样

Abstract: Regarding that the videos collected by intelligent monitoring system in coal mine underground are blur, a novel algorithm for unclear video object detection based on improved particle filter is proposed. In the framework of standard particle filter, a nonlinear, non-Gaussian and multi system state space fusion model is constructed based on frame difference. First, particle sampling and probability density propagation are realized in the region of the key points obtained in the frame difference image. Then, the weighted posterior sample particles are used to represent the posterior probability density of the multi system state space fusion model. The mean of the samples is used to estimate and fuse the posteriori state of the system. Finally, the system state space model is output to fulfill the object detection. Experiments are carried out using the video data of the Sanjiaohe coal mine underground monitoring system. The performance of the proposed model is evaluated by comparison with extended Kalman filter, unscented Kalman filter and particle filter. The improved accuracy and effectiveness of the proposed method are demonstrated.

Key words: information processing technology, object detection, particle filter, multi-state space, critical region sampling

中图分类号: 

  • TD76
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