Hou Tao1, Zhao Yanzhang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Niu Hongxia1, Chen Mingxi1, and Wang Shan1
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Received: September 15, 2021 Accepted: December 6, 2021 Publication Date: January 19, 2022
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
The intrusion of foreign object on the railway tracks directly affects the safe operation of the trains. Due to the complex railway tracks environment, the existing research on foreign object detection based on image processing has problems such as weak anti-noise, poor real-time performance, and low accuracy. A real-time detection and tracking method for foreign bodies invading railway tracks based on Hidden Markov Model (HMM) Kalman Filter is put forward in this paper. Firstly, the Gaussian Mixture Model is used to extract the feature vector of the object in multiple images and generate a feature sequence. Secondly, the feature sequence of the detected object is processed by the Hidden Markov Model, and the movement railway tracks of the foreign object is predicted. Finally, the prediction result is compared with the actual results. The Kalman filter is updated according to the comparison results, and the foreign objects invading the railway tracks are finally detected and tracked. The simulation results show that the method can detect accurately and quickly and track foreign objects invading the railway tracks. Compared with the existing foreign object detection results achieved by the application of neural networks and Gaussian Mixture Models, the processing and results of the algorithm have strong anti-noise performance and real-time performance. It has high definition and an accuracy rate of 98.73%, which can further ensure the safety of train operation.
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