Edge Intelligence for Real-Time Video Analytics: Architectures, Algorithms, and Applications

The proliferation of video cameras in smart cities, autonomous vehicles, retail environments, and industrial settings has led to an unprecedented surge in video data generation. Traditional cloud-centric video analytics approaches suffer from high bandwidth requirements, latency issues, and privacy concerns. Edge intelligence, which brings computation closer to data sources, offers a promising solution for real-time video analytics. This paper presents a comprehensive exploration of edge intelligence for real-time video analytics, encompassing architectures, algorithms, and applications. We propose EdgeVision, a novel three-tier architecture that combines lightweight neural network design, model compression techniques, and hardware-aware optimizations to enable efficient real-time video analytics on resource-constrained edge devices. The framework includes an adaptive pipeline orchestration mechanism that dynamically adjusts processing based on resource availability and application requirements. We introduce FastSeg, a new lightweight semantic segmentation algorithm that achieves ۸۷% accuracy on standard benchmarks while requiring only ۲.۳ GFLOPS of computation. Our system demonstrates the ability to process ۳۰ frames per second on embedded platforms with power consumption under ۵ watts. We evaluate EdgeVision across multiple applications, including traffic monitoring, retail analytics, and industrial inspection, demonstrating up to ۱۸× latency reduction and ۷۶% bandwidth savings compared to cloud-based alternatives, while maintaining comparable accuracy. The experimental results confirm that edge intelligence offers a viable path to real-time, scalable, privacy-preserving video analytics, particularly in scenarios with limited connectivity or strict latency requirements.

کلیدواژه ها:

Edge Computing ، Video Analytics ، Deep Learning ، Computer Vision ، Real-time Systems ، Edge-Cloud Collaboration ، Model Compression ، Embedded Systems