基于时空域与频域融合的农村变电站中运动目标检测算法

Moving object detection algorithm in rural substation based on time-space-frequency-domain

目前,中国农村的变电站通常规模比较大,电气设备都比较传统,设备自动化和智能化程度普遍较低,因此在变电站的日常运行维护中对人力资源的要求相对较高。在日常巡检与突发故障检修时,都需要工作人员进入现场操作。视频监测技术可以实现变电站工作人员监测的远程化、智能化、自动化,使工作人员的安全性大大提高,而运动目标检测是其中的关键。为了改善常用目标检测算法在农村变电站安全监测上存在的问题,该文提出了一种多域融合(时域空域与频域)的运动目标检测(time-domain space-domain and frequency-domain fusion,TSFF)算法。首先在时域上选取连续多帧图像,接着选取多帧图像相同位置处像素点构成时域信号,通过短时傅里叶变换在频域观察频率变化幅值,并结合空域上目标像素点水平与垂直4个方向上扩展邻域短时傅里叶变换频率变化幅值,最终判断出该像素点位置为背景、噪声或运动目标,完成运动目标分割。该方法较好地克服了传统背景差分法受到光照、阴影、噪声等变化的影响,相较于帧间差分法最大限度地保留了运动目标的信息,并克服了自适应背景建模算法对于出现高频扰动噪声检测效果较差的问题。试验表明,该算法在保留运动目标信息的同时,最大限度去除了背景。

Rural substation in China usually has large scale,but the electrical equipment has low automation and intelligentization.So the substation maintenance in the daily operation requires high amount of man powers.The staffs need to enter the substation to complete the daily patrol,check and maintenance.Video monitor system can be used,however,for remote and real-time monitoring to protect the on duty staff(moving targets)from danger.Moving target detection is one of the critical issues in video monitor system.In order to overcome the disadvantages of the commonly used object detection algorithm,in this paper,we proposed a moving object detection algorithm in rural substation based on multi-domain(time-domain space-domain and frequency-domain fusion TSFF).At first,the consecutive multi-frames were observed in time domain.Then a time-domain signal was constituted by multiple frames pixels in the same location to observe the amplitude of frequency variation by use of short time Fourier transform in frequency domain.Then the horizontal and vertical four directions extension neighborhood of each target pixel was selected in space domain.The amplitude of frequency variation of the pixels in transverse and longitudinal section was also calculated.Eventually the target pixel was detected as the background,noise,or moving target,to complete the moving object segmentation.Comparing with these kinds of algorithm,inter-frame difference(IFD)method can remove noise effectively(mean of accuracy rate was 96.73%),but the object information retention performed poorly(mean of recall rate was 44.14%).The FPCP algorithm performed generally in all aspects(mean of accuracy rate was 65.95%and mean of recall rate was 77.73%).The GRASTA algorithm performed poorly in object information retention when the background noise disturbance was large(the lowest recall rate was 21.83%).The GMM algorithm had the high mean of accuracy rate(85.31%),but for object information reserving had its shortcomings to a certain extent when the back-ground noise disturbance was large(the lowest recall rate was 78.18%).The ViBe algorithm had the low recall rate(mean of recall rate was 65.05%).The TSFF algorithm showed stable performance with different conditions and higher robustness.The TSFF algorithm had the optimal mean recall rate(86.10%)and mean accuracy rate(96.68%).The TSFF algorithm was not only to overcome the RPAC method affected by lights,shadows,and noise,but also to reserve the object information to the maximum compared with the inter-frame difference method and overcome the difficulty in dealing with the high frequency noise compared with the adaptive background modeling algorithm.The experiment results showed that the proposed algorithm reserve the object information well,and maximum removed the background.The TSFF algorithm was suitable for real-time and monitoring substation staff in different circumstances.