A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems

TL;DR

This survey reviews the architecture, techniques, and recent advances in distributed video analytics within cloud-edge-terminal systems, highlighting their applications, challenges, and future research directions.

Contribution

It provides a comprehensive overview of architectural frameworks, edge and cloud methods, hybrid approaches, and emerging trends like large language models in video analytics systems.

Findings

Edge-centric approaches focus on-device processing and offloading.

Hybrid systems optimize performance through adaptive resource management.

Recent advances include large language models and multimodal integration.

Abstract

The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis. Among multiple advantages, CETC systems can distribute video processing tasks and enable adaptive analytics across cloud, edge, and terminal devices, leading to breakthroughs in video surveillance, autonomous driving, and smart cities. In this survey, we first analyze fundamental architectural components, including hierarchical, distributed, and hybrid frameworks, alongside edge computing platforms and resource management mechanisms. Building upon these foundations, edge-centric approaches emphasize on-device processing, edge-assisted offloading, and edge intelligence, while cloud-centric methods leverage powerful computational capabilities for complex video understanding and model training. Our investigation also covers hybrid video analytics incorporating adaptive task offloading and resource-aware scheduling techniques that optimize performance across the entire system. Beyond conventional approaches, recent advances in large language models and multimodal integration reveal both opportunities and challenges in platform scalability, data protection, and system reliability. Future directions also encompass explainable systems, efficient processing mechanisms, and advanced video analytics, offering valuable insights for researchers and practitioners in this dynamic field.