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基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法

李成城 马立森 田原 贾运红 贾曲 田伟琴 张凯

李成城,马立森,田原,等. 基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法[J]. 工矿自动化,2023,49(5):66-73.  doi: 10.13272/j.issn.1671-251x.2022100002
引用本文: 李成城,马立森,田原,等. 基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法[J]. 工矿自动化,2023,49(5):66-73.  doi: 10.13272/j.issn.1671-251x.2022100002
LI Chengcheng, MA Lisen, TIAN Yuan, et al. An onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter[J]. Journal of Mine Automation,2023,49(5):66-73.  doi: 10.13272/j.issn.1671-251x.2022100002
Citation: LI Chengcheng, MA Lisen, TIAN Yuan, et al. An onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter[J]. Journal of Mine Automation,2023,49(5):66-73.  doi: 10.13272/j.issn.1671-251x.2022100002

基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法

doi: 10.13272/j.issn.1671-251x.2022100002
基金项目: 国家重点研发计划项目(2020YFB1314002)。
详细信息
    作者简介:

    李成城(1997—),男,山西运城人,硕士研究生,主要研究方向为视频稳像,E-mail:1718520210@qq.com

  • 中图分类号: TD632

An onboard video stabilization algorithm for roadheader based on CLAHE and Kalman filter

  • 摘要: 掘进机等煤机装备在行进或作业期间,易因车体振动引起机载相机视频模糊,导致基于机载视频的机器视觉检测精度和可靠性下降。针对该问题,提出一种基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法。该算法由运动估计、轨迹平滑和运动补偿3个部分组成。在运动估计阶段,先采用限制对比度自适应直方图均衡(CLAHE)算法对井下巷道图像进行增强处理,再利用Shi-Tomasi算法获取每帧图像的特征点,对获取的特征点进行光流追踪和匹配,进而计算出相机的运动轨迹。在轨迹平滑阶段,利用卡尔曼滤波,根据视频前一帧的最优值预测当前时刻值,避免均值滤波需预先存储采样数据的问题,提高稳像的实时性。在运动补偿阶段,根据原始运动路径和平滑路径的关系对抖动视频逐帧补偿,生成稳定的视频序列。实验结果表明:① 经CLAHE增强处理后,特征点匹配成功率比未增强处理时提高了58%,比HE增强处理时提高了43%,说明CLAHE算法可有效提高图像特征点匹配数。② 通过像素偏移分析、差分图分析、峰值信噪比(PSNR)分析,验证了基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法具有较好的稳像效果。③ 与传统的HE+均值滤波算法相比,基于CLAHE与卡尔曼滤波的算法处理100帧视频图像的整体耗时减少了0.379 s,在去除抖动的同时,有效提高了稳像的实时性。

     

  • 图  1  基于CLAHE与卡尔曼滤波的掘进机机载视频稳像算法流程

    Figure  1.  Process of onboard video stabilization algorithm for roadheader based on contrast limited adaptive histogram equalization and Kalman filter

    图  2  直方图剪切与分配过程

    Figure  2.  Histogram cutting and allocation process

    图  3  巷道图像增强前后对比

    Figure  3.  Comparison of roadway image before and after enhancement

    图  4  巷道图像增强前后特征点提取对比

    Figure  4.  Comparison of feature point extraction before and after roadway image enhancement

    图  5  基于CLAHE增强图像的特征跟踪

    Figure  5.  Feature tracking based on contrast limited adaptive histogram equalization enhanced image

    图  6  像素点水平及垂直方向运动轨迹

    Figure  6.  Horizontal and vertical motion trajectories of pixel points

    图  7  相邻帧特征点匹配结果

    Figure  7.  Matching results of adjacent frame feature points

    图  8  稳像前后水平方向偏移对比

    Figure  8.  Comparison of horizontal deviation before and after image stabilization

    图  9  稳像前后差分图

    Figure  9.  Difference map before and after image stabilization

    图  10  稳像前后PSNR值对比

    Figure  10.  Comparison of PSNR values before and after image stabilization

    表  1  滤波前后方差

    Table  1.   Variance before and after filtering

    方向滤波前方差滤波后方差
    水平1.1210.23
    垂直2.8201.79
    下载: 导出CSV

    表  2  各算法耗时

    Table  2.   Time consuming of each algorithm s

    算法运动估计耗时运动平滑耗时相关计算耗时整体耗时
    HE+均值滤波2.3400.0031.2863.629
    本文
    算法
    1.9380.0021.3103.250
    下载: 导出CSV
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  • 收稿日期:  2022-10-05
  • 修回日期:  2023-05-15
  • 网络出版日期:  2022-12-05

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