In the past decade the number of rail accidents have increased dramatically. According to a study conducted in 2016-2017, 74 percent of the cases of rail accidents are because of lack of alertness(drowsiness) or negligence of the train drivers. According to a research 32000 animals were killed on the railway tracks between 2016 and 2018. Our paper gives complete solution to above mentioned critical issues using computer vision technology. i)Driver drowsiness is detected using an IR LED based tracking approach so that drowsiness can be detected in odd hours(mid-night) also, which is fitted inside the engine cab. This method is based on the spatial temporal relationship which would detect the drowsiness more accurately. If the drowsiness is detected then a level I alarm is sounded. ii)Using a long range camera which is placed on the train engine, images are continuously monitored using streaming technique with a well-trained object detection deep learning model using tensor flow as back-end to detect obstacles on the train track. Once an obstacle is detected a level II alarm is sent to the driver. This automized system helps the driver to be more productive in his job by helping him in the emergency situations to act quickly and in turn help save lives. In addition to saving lives this system also helps the Indian Railways in terms of monetary benefits by saving a lot of operational cost.

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