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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Calcutta
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20210930T082621Z
UID:5379D683-9C31-41F7-BCDC-1862AEABB422
DTSTART;TZID=Asia/Calcutta:20210825T110000
DTEND;TZID=Asia/Calcutta:20210825T120000
DESCRIPTION:In this talk\, we will present and discuss the current trends a
 nd researches in video analytics. As surveillance cameras have been widely
  installed worldwide\, although the main purpose of those cameras is for m
 onitoring\, the most important task is to be able to analyze video content
 s and extract useful information. Artificial Intelligence (AI) and deep le
 arning-based computer vision techniques utilizing multi-layer neural netwo
 rks are drastically improving the performance of video analytics to a cert
 ain extent. Several on-going researches on deep learning-based video analy
 tics such as image super resolution\, real-time multiple face recognition 
 system\, video anomaly detection and several implementations of embedded v
 ideo analytic systems on FPGA and Single Board Computers will be discussed
 . Some use cases of utilizing video analytics will also be mentioned.\n\nC
 o-sponsored by: Indus University\n\nSpeaker(s): Supavadee\, \n\nVirtual: h
 ttps://events.vtools.ieee.org/m/280006
LOCATION:Virtual: https://events.vtools.ieee.org/m/280006
ORGANIZER:hansa.shingrakhia.2013@ieee.org
SEQUENCE:1
SUMMARY:WISP TALK SERIES : AI-based Video Analytics
URL;VALUE=URI:https://events.vtools.ieee.org/m/280006
X-ALT-DESC:Description: <br /><p><span style="font-size: 12pt\; font-family
 : 'Times New Roman'\,serif\;">In this talk\, we will present and discuss t
 he current trends and researches in video analytics.&nbsp\; As surveillanc
 e cameras have been widely installed worldwide\, although the main purpose
  of those cameras is for monitoring\, the most important task is to be abl
 e to analyze video contents and extract useful information. Artificial Int
 elligence (AI) and deep learning-based computer vision techniques utilizin
 g multi-layer neural networks are drastically improving the performance of
  video analytics to a certain extent. Several on-going researches on deep 
 learning-based video analytics such as image super resolution\, real-time 
 multiple face recognition system\, video anomaly detection and several imp
 lementations of embedded video analytic systems on FPGA and Single Board C
 omputers will be discussed. Some use cases of utilizing video analytics wi
 ll also be mentioned.</span></p>
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END:VCALENDAR