Application of optical flow in locomotive safe driving
光流法在机车安全行驶中的应用

In the video-based locomotive safe driving assistant system, reducing the detecting region of signal light according to the location of rails and some prior knowledge can improve the efficiency and reliability of signal light detection. But it possibly fails to detect rail in some complicated illumination environments (especially in the night), and the signal light detecting region cannot be available. To solve this problem, this paper proposed an optical flow based approach for signal light region prediction. Firstly, it got the strong corners in region of interest by KLT operators, and calculated their optical-flow using Lucas-Kanade method with pyramids. After that it could get the locomotive turning state from the optical flow statistical information. In the end, it could dynamically estimate the signal light region according to the turning states and prior knowledge. Besides, it also proposed a method for rails false-tracking detecting by utilizing locomotive turning states, which could detect the tracking error timely and arouse the rails relocation. Experimental results demonstrate that the proposed method can estimate the signal region effectively and make the signal detection more real-time and more robust against disturbance.