Year: 2025
Type: article
Abstract: The widespread adoption of smart cameras in areas such as security monitoring, intelligent transportation, and industrial quality inspection is fueled by the growing number of Internet of Things (IoT) devices and continuous progress in artificial intelligence (AI) algorithms. However, traditional video surveillance systems rely on cloud computing and face challenges like limited bandwidth, high latency, and privacy risks. Edge computing, a distributed architecture that brings computation and storage closer to the data source, offers an effective way to improve smart camera video processing and recognition. Therefore, the paper investigates smart camera video processing and recognition systems based on edge computing reviewing key technologies and implementation methods via recent research and typical applications. In addition, it examines the progress and features of edge platform architecture, video analysis and object detection algorithms, resource scheduling and energy management, model compression, and data security and privacy protection. The results show that edge-intelligent video systems are effective in reducing network load, lowering response latency, and improving the security of local data processing. However, they still face technical challenges in heterogeneous resource management, real-time scheduling, and multi-task collaboration. As such, this paper further reviews the main existing issues and offers a practical outlook on system optimization and future applications.
Author: Xiaoxu Chen
Institution: Hubei University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414147648