Affiliation:
1. University of California and Technische Universität Berlin
2. University of California
Abstract
The efficient processing of video streams is a key component in many emerging Internet of Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self-driving cars. These applications require real-time high-throughput video processing. This can be attained via a collaborative processing model between the edge and the cloud---called an
Edge-Cloud model
. To this end, many approaches were proposed to optimize the latency and bandwidth consumption of Edge-Cloud video processing, especially for Neural Networks (NN)-based methods. In this demonstration. We investigate the efficiency of these NN techniques, how they can be combined, and whether combining them leads to better performance. Our demonstration invites participants to experiment with the various NN techniques, combine them, and observe how the underlying NN changes with different techniques and how these changes affect accuracy, latency and bandwidth consumption.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
36 articles.
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1. JAVP: Joint-Aware Video Processing with Edge-Cloud Collaboration for DNN Inference;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26
2. The state of art and review on video streaming;Journal of High Speed Networks;2023-08-14
3. Multiple Early-Exits Strategy for Distributed Deep Neural Network Inference;2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT);2023-06
4. Blockchain-based Collaborative Edge Intelligence for Trustworthy and Real-Time Video Surveillance;IEEE Transactions on Industrial Informatics;2023-02
5. Intelligent video surveillance with edge-cloud collaboration based on scheduling policy;2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE);2023-01-06