e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.

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 96.74%), but the object information retention performed poorly (mean of recall rate was 41.01%). The FPCP algorithm performed generally in all aspects (mean of accuracy rate was 65.58% and mean of recall rate was 75.63%). 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 (85.88%), 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 65.07%). The TSFF algorithm showed stable performance with different conditions and higher robustness. The TSFF algorithm had the optimal mean recall rate (86.48%) and mean accuracy rate (96.26%). 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. [ABSTRACT FROM AUTHOR]