A Decade of Video Analytics at Edge: Training, Deployment, Orchestration, and Platforms
Tong Bai, Haoran Zhao, Lei Huang, Zhipeng Wang, Dong In Kim, Arumugam Nallanathan
Abstract
Video analytics (VA), capable of autonomously understanding events in video content, has demonstrated significant potential across various applications, from surveillance to self-driving cars and industrial automation. However, traditional VA, relying on either end-device or cloud-based solutions, faces limitations such as restricted on-device computing power and network congestion at cloud centers. Edge computing offers a promising solution, enabling low-latency, high-accuracy, and bandwidth-efficient performance, thus supporting the rapid growth of VA deployment. This article provides a comprehensive review of VA at the edge, examining aspects of model training, deployment, end-edge-cloud orchestration, and VA platforms. Specifically, we explore model training approaches conducted in the cloud, at the edge, and in hybrid cloud-edge configurations. We also discuss various model deployment techniques, including quantization and network pruning. Furthermore, the article surveys end-edge-cloud orchestration strategies, categorized into VA query offloading and query scheduling. We evaluate practical deployments and review the literature on VA platforms. Finally, we outline several promising future research directions for advancing this field.