【摘要】 为解决煤层气开采(CBM)现场中抽水机往复运动和风吹草动等动态环境对前景检测的干扰及核密度估计(KDE)目标检测法实时性差的问题,提出了一种改进核密度估计前景检测算法。该方法先用背景差分法(BS)融合三帧差算法将图像分割成动态背景区与非动态背景区,对于动态背景区再用核密度算法分割前景。分割前景时提出了一种新的动态阈值求取方法,综合了相邻样本绝对差均值和样本方差来确定窗宽,并用定时更新与实时更新相结合的策略更新第二背景模型,在替换样本时用随机抽取策略代替先进先出(FIFO)方式。仿真结果表明,改进核密度估计算法与核密度估计法和背景差分核密度估计(BS-KDE)法相比,平均每帧图像算法耗时分别降低了94.18%和15.38%,识别的运动目标也更为完整。实验结果表明所提算法在煤层气开采场景中能准确检测到前景,并基本满足标清视频监控实时性要求。更多还原

【Abstract】 A new improved Kernel Density Estimation( KDE) algorithm used to segment foreground was proposed for the problem of reciprocating pumps and other troubles for segmenting foreground in the field of Coal Bed Methane( CBM)extraction and poor real-time of KDE. Background Subtraction( BS) and three frame difference were applied to divide the image into dynamic and non-dynamic background regions and then KDE was used to segment foreground for the dynamic background region. A new method of determining dynamic threshold was proposed when segmenting foreground region. Mean absolute deviation over the sample and sample variance were combined to compute the bandwidth. And the strategy of combining regular update with real-time update was used to renew the second background model. Random selection strategy instead of First In First Out( FIFO) mode was applied when replacing samples of the second background model. In the simulation experiments, the average time-consuming of per frame image is reduced by 94. 18% and 15. 38% and moving objects are more complete when comparing the improved KDE with the KDE and Background Subtraction Kernel Density Estimation( BS-KDE) respectively. The experimental results show that the proposed algorithm can detect foreground in the field of CBM extraction accurately and meet the real-time requirement in the standard definition video surveillance system basically.更多还原