he application of artificial intelligence to digital video is unlocking previously unseen insights, revealing new ways organizations can improve safety and boost productivity.
The wide applicability of video analytics is demonstrated, perhaps unexpectedly, by its use in the art world. Generative adversarial networks, a type of AI, have evolved to the point thatthat are altogether new or evocative of a well-known master. By having eyes in the sky, on the conveyor belt, in parts bins and on the assembly floor, organizations can see not only which elements of a process are falling out of sync but also how changes could cascade and cause disruption. Video recognition can identify if parts are being delivered to the wrong locations and if defects are not being eliminated early enough.
Cameras mounted around the manufacturing floor unobtrusively monitor parts as they flow throughout the assembly process. One camera observes that parts are hitting rollers with greater force than expected. Video analysis reveals that the problem went unreported by workers because the parts traveling the line were undamaged.
It can give city planners and policymakers an understanding of service quality and stability that would take a survey team weeks or longer to prepare. By examining city streets from fixed cameras, such as those perched on streetlights or mounted to public vehicles, video analytics can spot potholes in the making and dial up repair teams before they become dangerous. It can also spot places the garbage truck or street sweeper may have missed.
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