基于自适应高斯混合模型的铁轨异物入侵检测研究

Research on detection of foreign object intrusion in railroad tracks based on AGMM

针对复杂铁路环境下异物入侵动态检测抗扰能力弱和快速性差等问题,本文提出了一种基于自适应高斯混合模型(adaptive Gaussian mixture model, AGMM)的铁轨异物入侵检测方法。通过分析铁路场景发生复合抖动时存在随机性的特点,首先对输入的铁路视频进行抖动检测,然后引入仿射变换与中值滤波,对视频序列中存在抖动的图像进行处理。再采用逐帧迭代填充的方法,对去抖后图像出现的黑边进行填充,得到无抖动和无黑边的铁路视频帧。最后,在现有高斯混合模型的基础上,设计了自适应选择高斯分布个数和学习率,利用改进后的高斯混合模型实现复杂铁路视频的背景建模,并由此提高前景目标的检测速度。实验结果表明,本文方法在铁路视频存在抖动的情形下,轨道异物入侵目标检测的精度是原有的2.6倍,检测速度是2.8倍,能提高目标检测的抗干扰性和快速性。

Aiming at the problems of weak anti-interference ability and poor speed of dynamic foreign object detection in complex railway environment, a rail foreign object intrusion detection method based on an adaptive Gaussian mixture model(AGMM) is put forward in this paper.By analyzing the characteristics of randomness when compound jitter occurs in railway scenes, Firstly, jitter detection on the input railway video is performed, and then affine transformation and median filtering are introduced to process the jittery images in the video sequence.Secondly, the method of iterative filling frame by frame is used to fill the black edges of the image after debounce to obtain a railway video frame without jitter and without black edges.Finally, on the basis of the existing Gaussian mixture model, an adaptive selection of the number of Gaussian distributions and learning rate is designed, and the improved Gaussian mixture model is used to realize the background modeling of complex railway videos, and thereby improve the detection speed of foreground objects.The experimental results show that in the case of jitter in the railway video, the accuracy rate of the track foreign body intrusion target detection is 2.6 times, and the detection speed is 2.8 times that of the original algorithm, which can improve the anti-interference and rapidity of target detection.