Thanks to advances in computer vision and deep neural networks (DNNs) in what can arguably be described as the golden age of vision, AI, and machine learning, video analytics systems\u2014systems performing analytics on live camera streams\u2014are becoming more accurate. This accuracy offers opportunities to support individuals and society in exciting ways, like informing homeowners when a package has been delivered outside their door, allowing people to give their pets the attention they need when out for the day, and detecting high-traffic areas so cities can consider adding a stop light.<\/p>\n
While DNN advancements and DNN inference are enablers, they alone are not enough when it comes to extracting valuable insights from live videos. Live video analytics requires keeping up with video frame rates, which can be as fast as 60 frames per second, making it crucial to effectively filter frames and avoid the costly processing of each frame. Project Rocket provides a framework to do exactly that.<\/p>\n","protected":false},"excerpt":{"rendered":"
Thanks to advances in computer vision and deep neural networks (DNNs) in what can arguably be described as the golden age of vision, AI, and machine learning, video analytics systems\u2014systems performing analytics on live camera streams\u2014are becoming more accurate. 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