Distributed Real-Time Object Detection Based on Edge-Cloud Collaboration for Smart Video Surveillance Applications

作     者：Chen, Yung-Yao Lin, Yu-Hsiu Hu, Yu-Chen Hsia, Chih-Hsien Lian, Yi-An Jhong, Sin-Ye 

作者机构：Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei 106335 Taiwan Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 106344 Taiwan Providence Univ Dept Comp Sci & Informat Management Taichung 43301 Taiwan Natl Ilan Univ Dept Comp Sci & Informat Engn Ilan 260007 Taiwan Natl Cheng Kung Univ Dept Engn Sci Tainan 701 Taiwan 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：93745-93759页

核心收录：

基　　金：Ministry of Science and Technology  Taiwan [MOST 111-2221-E-027-050-  MOST 111-3116-F-006-005-  MOST 111-3116-F-027-001-] 

主　　题：Image edge detection Cloud computing Real-time systems Video surveillance Artificial intelligence Collaboration Media Cloud computing edge computing edge-cloud collaboration object detection video surveillance 

摘      要：Internet of Things (IoT) and artificial intelligence (AI) can realize the concept of smart city. Video surveillance in smart cities is, usually, based on a centralized framework in which large amounts of real-time media data are transmitted to and processed in the cloud. However, the cloud relies on network connectivity of the Internet that is sometimes limited or unavailable;thus, the centralized framework is not sufficient for real-time processing of media data needed for smart video surveillance. To tackle this problem, edge computing - a technique for accelerating the development of AIoT (AI across IoT) in smart cities - can be conducted. In this paper, a distributed real-time object detection framework based on edge-cloud collaboration for smart video surveillance is proposed. When collaborating with the cloud, edge computing can serve as converged computing through which media data from distributed edge devices of the network are consolidated by AI in the cloud. After AI discovers global knowledge in the cloud, it to be shared at the edge is deployed remotely on distributed edge devices for real-time smart video surveillance. First, the proposed framework and its preliminary implementation are described. Then, the performance evaluation is provided regarding potential benefits, real-time responsiveness and low-throughput media data transmission.