  Deep Learning methods have been extensively used to analyze video data to
extract valuable information by classifying image frames and detecting objects.
We describe a unique approach for using video feed from a moving Locomotive to
continuously monitor the Railway Track and detect significant assets like
Switches on the Track. The technique used here is called Siamese Networks,
which uses 2 identical networks to learn the similarity between of 2 images.
Here we will use a Siamese network to continuously compare Track images and
detect any significant difference in the Track. Switch will be one of those
images that will be different and we will find a mapping that clearly
distinguishes the Switch from other possible Track anomalies. The same method
will then be extended to detect any abnormalities on the Railway Track. Railway
Transportation is unique in the sense that is has wheeled vehicles, Trains
pulled by Locomotives, running on guided Rails at very high speeds nearing 200
mph. Multiple Tracks on the Rail network are connected to each other using an
equipment called Switch or a Turnout. Switch is either operated manually or
automatically through command from a Control center and it governs the movement
of Trains on different Tracks of the network. Accurate location of these
Switches is very important for the railroad and getting a true picture of their
state in field is important. Modern trains use high definition video cameras
facing the Track that continuously record video from track. Using a Siamese
network and comparing to benchmark images, we describe a method to monitor the
Track and highlight anomalies.
