Vision-Based Train Position and Movement Estimation Using a Fuzzy Classifier
Jae-Won Song, Tae-Ki An, Dae-Ho Lee · Journal of Digital Convergence · 2012
We propose a vision-based method that estimates train position and movement for railway monitoring in which we use a fuzzy classifier to determine train states. The proposed method employs frame difference and background subtraction for estimating train motion and presence, respectively. These features are used as the linguistic variables of the fuzzy classifier. Experimental results show that the proposed method can correctly estimate train position and movement. Therefore the method can be used for railway monitoring systems which estimate crowd density or protect safety.