Ravindran, A.A. Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead. IoT2023, 4, 486-513.
Ravindran, A.A. Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead. IoT 2023, 4, 486-513.
Ravindran, A.A. Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead. IoT2023, 4, 486-513.
Ravindran, A.A. Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead. IoT 2023, 4, 486-513.
Abstract
The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.
Keywords
video analytics; edge computing; streaming video; systems; deep learning; AI; latency; bandwidth; privacy
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
