Nokia is using machine learning-based artificial intelligence (AI) to identify potential issues at railroad crossings in real time.
Japan's Odakyu Electric Railway is conducting trials of Nokia’s SpaceTime scene analytics in order to identify methods for improving rail crossing safety. With testing underway at Tamagawa Gakuenmae No.8 railroad crossing in Machida City, Tokyo, Nokia’s SpaceTime scene analytics can detect abnormal events by applying machine learning-based artificial intelligence to available camera images. The trials started last week, and will run through March.
Nokia's SpaceTime analytics, which was developed by Nokia Bell Labs, monitors railroad crossings across Odakyu’s 120.5 kilometers of tracks, which includes 229 crossing points and 137 radar systems for object detection.
By analyzing image feeds that are generated by conventional railroad crossing cameras, the analytics program identifies potential issues before they arise. Nokia's SpaceTime analytics can also reduce bandwidth requirements at remote sites that could have limited connectivity.
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Nokia SpaceTime scene analytics can also provide real-time alerts for unauthorized entry into remote facilities. It can detect and alert supervisors when personnel or equipment access unsafe locations in industrial settings or when heavy machinery is out of position creating a potential hazard.
"Network connected cameras are one of the most prolific sources of IoT data that can provide valuable insights to help promote high safety standards," said John Harrington, head of Nokia Japan, in a statement. "By running machine learning analytics on camera feeds, and sending solely relevant scenes and events to operators, the full benefits of video surveillance can be realized in a wide variety of settings, with rail crossings a particularly relevant use case.”