Edge Computing and Communication Systems for Video Analytics Applications
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 contents; conducts preprocessing and sends intermediate results to the cloud to complete the analytics tasks. There are diverse requirements 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 system designs. In particular, we study edge-side acceleration using new hardware and the designs on resource-efficient privacy-preserving systems. We also present experiences in broadening the impact of research results into industry projects.
Date and Time
Location
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Registration
- Date: 14 Nov 2022
- Time: 01:00 PM to 02:00 PM
- All times are (GMT-05:00) US/Eastern
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- 218 Central Avenue Newark, NJIT
- Newark, New Jersey
- United States 07102
- Building: GITC 4402
- Room Number: GITC 4402
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- Co-sponsored by New Jersey Institute of Technology
- Starts 02 November 2022 10:07 AM
- Ends 14 November 2022 02:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Dr. Dan Wang of Department of Computing, The Hong Kong Polytechnic University.
Edge Computing and Communication Systems for Video Analytics Applications
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 contents; conducts preprocessing and sends intermediate results to the cloud to complete the analytics tasks. There are diverse requirements 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 system designs. In particular, we study edge-side acceleration using new hardware and the designs on resource-efficient privacy-preserving systems. We also present experiences in broadening the impact of research results into industry projects.
Biography:
Dr. Dan Wang is currently a professor at Department of Computing, The Hong Kong Polytechnic University. His research interests lie in networked systems, and recently in the inter-discipline domains of smart energy systems. He publishes in ACM SIGCOMM, ACM SIGMETRICS, IEEE INFOCOM and in inter-discipline conferences, such as ACM e-Energy, ACM Buildsys. He won the Best Paper Award of ACM e-Energy 2018 and the Best Paper Award of ACM Buildsys 2018. He is currently the steering committee chair of IEEE/ACM IWQoS and the steering committee chair of ACM e-Energy. He is an advisor of EMSD, the Hong Kong SAR government. He has extensive experiences in applying his research results to industry, including Huawei, IBM, Henderson, etc. He won the TechConnect Global Innovation Award in 2017.
Address:Hong Kong
Agenda
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 contents; conducts preprocessing and sends intermediate results to the cloud to complete the analytics tasks. There are diverse requirements 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 system designs. In particular, we study edge-side acceleration using new hardware and the designs on resource-efficient privacy-preserving systems. We also present experiences in broadening the impact of research results into industry projects.