物聯網與人工智慧的結合引領新的風潮，將“智慧城市”的概念從理想變成現實。 其中影像監控在智慧城市中是十分重要的應用，通常是採用雲運算的方式實現，然而雲運算是基於集中式框架會將收集到的資料集中到雲端處理，優點在於雲端具有強大的運算資源，可以大量分析及處理資料，缺點在於無法即時分析與處理資料。因此，本文提出了基於深度學習與分散式框架之智慧監控系統，並進行了初步設置。本文中提出使用邊緣運算的技術來改善集中式框架的問題，因為邊緣運算可以在本地端執行人工智慧，此技術可以用來加速AIoT（AI 結合 IoT）的發展，並在智慧城市中應用於不同的領域，使人工智慧落地。 其中，本文們結合邊緣運算與雲端運算形成邊緣雲計算，通過在分散式邊緣節點上的人工智慧的將本地端的知識整合到雲端，並在雲端實現全球知識的人工智慧，並將全球知識分享給在分散式邊緣節點上的人工智慧進行即時影像監控。

The combination of IoT and artificial intelligence (AI) leads a new trend, turning the concept of "smart city" from an ideal into a reality. Among them, image monitoring is a very important application in smart cities. It is usually implemented by cloud computing. However, cloud computing is based on a centralized framework that will collect collected data in the cloud for processing. The advantage is that the cloud has powerful computing resources. It can analyze and process a large amount of data, but the disadvantage is that it cannot analyze and process data in real time. Therefore, this paper proposes an intelligent monitoring system based on deep learning and a decentralized framework, and conducts preliminary settings. This paper proposes the use of edge computing technology to improve the problem of centralized framework, because edge computing can perform AI on the local side, this technology can be used to accelerate the development of AIoT (AI combined with IoT) and applied in different smart cities the field of AI. Among them, this paper combines edge computing and cloud computing to form edge cloud computing, integrates local knowledge into the cloud through AI on distributed edge nodes, realizes AI of global knowledge in the cloud, and shares global knowledge with AI on distributed edge nodes for real-time video surveillance.

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