Real-Time Video Analytics: The Killer App for Edge Computing

By Ganesh Ananthanarayanan, Paramvir Victor Bahl, Peter Bodík, Krishna Chintalapudi, Matthai Philipose, Lenin Ravindranath, Sudipta Sinha. Computer, 2017

492 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/267d7e2b1398ae3508fcee19027c0d98e272519c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xu Chen
2. Zhi Zhou
3. Liekang Zeng
4. En Li
5. Ganesh Ananthanarayanan
6. Junshan Zhang
7. Peter Bodík
8. Ion Stoica

In Computer Vision and Pattern Recognition

1. Xu Chen
2. Liekang Zeng
3. Zhi Zhou
4. En Li
5. Wei Yu
6. Peng Li
7. M. G. Sarwar Murshed
8. Xiaofei Liao

Citations in the corpus

• All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “Microsoft Research in their paper [335] has developed a realtime video analytics system which relies on edge computing.”
• A Survey on the Edge Computing for the Internet of Things (2018, methodology): “As discussed before, fast transmission time is the one of important benefits of edge computing, which can satisfy the QoS of time-sensitive applications, like the ‘‘Live Video Analytics’’ project from Microsoft [50].”
• Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “of Things (IoT) devices will be in use by 2025 [2], and such multitudes of wireless connected devices are enabling many compelling new applications, such as real-time video analytics [3], [4], augmented/virtual reality [5], and smart cities [6], which are computation intensive and delay sensitive an”
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, unknown): “can potentially be life threatening [13].”
• Chameleon: scalable adaptation of video analytics (2018, background): “As we move to the scenario of edge camera analytics [13], such a separation would be hard to achieve.”
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019, background): “By comparison, real-time video analytics is envisioned to be a killer application for edge computing [12], [37], [38].”
• Machine Learning at the Network Edge: A Survey (2019, background): “Rocket has been shown to be effective in a variety of applications [10], which include a ) a smart crosswalk for pedestrians in a wheelchair, b ) a connected kitchen to pre-make certain food to reduce customer wait-times, c ) traffic dashboard for raising an alarm in abnormal traffic volumes, and d ”
• Computation Offloading Toward Edge Computing (2019, background): “The latency of public cloud providers is usually over 100 ms [9], which would be disastrous for latency-sensitive applications, such as autonomous driving and real-time video analytics [10].”