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DTSTAMP:20221115T010352Z
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DESCRIPTION:There are an increasing number of video analytics applications\
 , such as metaverse games\, auto-driving\, etc. As a way to support these 
 applications\, much attention is being paid to edge-cloud video analytics 
 systems. The standard pipeline is one in which the edge captures video con
 tents\; conducts preprocessing and sends intermediate results to the cloud
  to complete the analytics tasks. There are diverse requirements from appl
 ications\, such as latency\, privacy\, etc.\, as well as diverse constrain
 ts from the resources in computing and communication. These make edge-clou
 d video analytics system designs challenging. In this talk\, we will discu
 ss issues related to edge-cloud video analytics system designs. In particu
 lar\, we study edge-side acceleration using new hardware and the designs o
 n resource-efficient privacy-preserving systems. We also present experienc
 es in broadening the impact of research results into industry projects.\n\
 nCo-sponsored by: New Jersey Institute of Technology\n\nSpeaker(s): Dr. Da
 n Wang\, \n\nAgenda: \nThere are an increasing number of video analytics a
 pplications\, such as metaverse games\, auto-driving\, etc. As a way to su
 pport these applications\, much attention is being paid to edge-cloud vide
 o analytics systems. The standard pipeline is one in which the edge captur
 es video contents\; conducts preprocessing and sends intermediate results 
 to the cloud to complete the analytics tasks. There are diverse requiremen
 ts from applications\, such as latency\, privacy\, etc.\, as well as diver
 se constraints from the resources in computing and communication. These ma
 ke edge-cloud video analytics system designs challenging. In this talk\, w
 e will discuss issues related to edge-cloud video analytics system designs
 . In particular\, we study edge-side acceleration using new hardware and t
 he designs on resource-efficient privacy-preserving systems. We also prese
 nt experiences in broadening the impact of research results into industry 
 projects.\n\nRoom: GITC 4402\, Bldg: GITC 4402\, 218 Central Avenue Newark
 \, NJIT\, Newark\, New Jersey\, United States\, 07102
LOCATION:Room: GITC 4402\, Bldg: GITC 4402\, 218 Central Avenue Newark\, NJ
 IT\, Newark\, New Jersey\, United States\, 07102
ORGANIZER:zhao@fdu.edu
SEQUENCE:4
SUMMARY:Edge Computing and Communication Systems for Video Analytics Applic
 ations
URL;VALUE=URI:https://events.vtools.ieee.org/m/330949
X-ALT-DESC:Description: <br /><p>There are an increasing number of video an
 alytics applications\, such as metaverse games\, auto-driving\, etc. As a 
 way to support these applications\, much attention is being paid to edge-c
 loud video analytics systems. The standard pipeline is one in which the ed
 ge captures video contents\; conducts preprocessing and sends intermediate
  results to the cloud to complete the analytics tasks. There are diverse r
 equirements from applications\, such as latency\, privacy\, etc.\, as well
  as diverse constraints from the resources in computing and communication.
  These make edge-cloud video analytics system designs challenging. In this
  talk\, we will discuss issues related to edge-cloud video analytics syste
 m designs. In particular\, we study edge-side acceleration using new hardw
 are and the designs on resource-efficient privacy-preserving systems. We a
 lso present experiences in broadening the impact of research results into 
 industry projects.</p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>There a
 re an increasing number of video analytics applications\, such as metavers
 e games\, auto-driving\, etc. As a way to support these applications\, muc
 h attention is being paid to edge-cloud video analytics systems. The stand
 ard pipeline is one in which the edge captures video contents\; conducts p
 reprocessing and sends intermediate results to the cloud to complete the a
 nalytics tasks. There are diverse requirements from applications\, such as
  latency\, privacy\, etc.\, as well as diverse constraints from the resour
 ces in computing and communication. These make edge-cloud video analytics 
 system designs challenging. In this talk\, we will discuss issues related 
 to edge-cloud video analytics system designs. In particular\, we study edg
 e-side acceleration using new hardware and the designs on resource-efficie
 nt privacy-preserving systems. We also present experiences in broadening t
 he impact of research results into industry projects.</p>\n<p>&nbsp\;</p>
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