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VisionTrack Launches AI-Powered Video Analysis Solution
AI telematics provider VisionTrack debuted a solution integrating telematics and artificial intelligence to help save lives and reinforce the company's road safety commitment to reduce traffic fatalities.

Compatible with any connected camera technology, the NARA solution provides proactive risk intervention and accurate incident validation to improve commercial fleet safety on the roads.
Photo: VisionTrack
VisionTrack, a provider of AI video telematics and connected fleet data, has launched an AI-powered post-analysis solution for commercial fleet safety, integrating vehicle camera footage and NARA (Notification, Analysis, and Risk Assessment) software to help vehicle operators reduce road deaths and injuries.
NARA is device agnostic so it can be integrated with existing connected camera technology – whether VisionTrack or third-party hardware – and adds another layer of analysis to AI vehicle cameras, installed with edge-based AI technology, that are often limited by the processing capacity of the device.
“Our cloud-based NARA software is a true game changer in the world of video telematics as it will help save time, costs, and most importantly lives, by providing proactive risk intervention and accurate incident validation,” explains Richard Kent, president of global sales at VisionTrack in a statement.
How NARA Works
NARA uses computer vision models with sensor fusion to assess footage of driving events, near misses, and collisions. This ensures the review process is manageable and timely, while eliminating human availability or error, so vehicle operators can make best use of video telematics insight to better protect road users and help prevent collisions.
During the testing phase, a 1,100-strong logistics fleet was found to be generating on average 2,000 priority videos a week, which would typically take someone over eight hours to review. NARA reduced the time needed to review events that require human validation to just minutes per day.
Advanced object recognition uses deep learning algorithms to automatically identify different types of vehicles, cyclists, and pedestrians. It will be able to distinguish between collisions, near misses, and false positives that can be generated by harsh driving, potholes, or speed humps. The software will also include Occupant Safety Rating that uses a range of parameters to calculate the percentage probability of injury and immediately identify if a driver needs assistance.
“NARA proactively removes false positives and monitors driver behavior, without the need for human involvement. With traditional video telematics solutions, commercial fleets can be experiencing hundreds of triggered daily events, so this will enable them to deliver more efficient working, while not compromising on road safety,” concludes Kent.
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