BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Pacific Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20191004T171630Z DESCRIPTION:NoTraffic optimizes traffic lights in real-time using multiple sensors\, preparing the road for the autonomous era. Our system enables ci ties to implement traffic policies\, operating autonomously in order to ma ximize traffic flow\, reduce congestion and carbon dioxide emissions\, pri oritize different vehicle types\, and prevent accidents. We’ll demonstra te how we utilize NVIDIA’s hardware and GPU-accelerated frameworks on ou r edge devices in order to optimize traffic while dealing with the constra ints of IoT. We’ll describe how we fuse multiple deep networks in order to apply existing computer vision concepts to real world scenarios. Our ta lk will tackle the noisy and biased data which is rarely addressed in most research papers and datasets\, but is inherent in every real world proble m. We’ll show we apply active learning in order to constantly optimize o ur training and data collection pipelines for continuous deployment of new \nmodels.\n \nIf you haven’t registered for GTC DC yet\, you can do so here .\n DTEND;TZID="Pacific Standard Time":20191105T152000 DTSTAMP:20191004T171630Z DTSTART;TZID="Pacific Standard Time":20191105T143000 LAST-MODIFIED:20191004T171630Z LOCATION:1300 Pennsylvania Ave NW\, Washington\, D.C. 20004 | Polaris PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:From Theory to Practice: Computer Vision on Edge Dev ices for Real-Time Traffic Optimization TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000709C51149B7AD501000000000000000 0100000006F0954BA4D4ADC4EB9324EF9DE921CB5 X-ALT-DESC;FMTTYPE=text/html:

NoTraffic optimizes traffic lights in real-time using multiple sensors \, preparing the road for the autonomous era. Our system enables cities to implement traffic policies\, operating autonomously in order to maximize traffic flow\, reduce congestion and carbon dioxide emissions\, prioritize different vehicle types\, and prevent accidents. We’\;ll demonstrate how we utilize NVIDIA’\;s hardware and GPU-accelerated frameworks on our edge devices in order to optimize traffic while dealing with the cons traints of IoT. We’\;ll describe how we fuse multiple deep networks i n order to apply existing computer vision concepts to real world scenarios . Our talk will tackle the noisy and biased data which is rarely addressed in most research papers and datasets\, but is inherent in every real worl d problem. We’\;ll show we apply active learning in order to constant ly optimize our training and data collection pipelines for continuous depl oyment of new

models.

 \;

If you haven’\;t registered for GTC DC yet\, you can do so here.

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