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

Abstract

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 lowthroughput media data transmission.

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

“…In Ref. [74], Chen et al introduced a distributed real-time object detection framework for video surveillance systems. This approach allows edge nodes to perform object detection using a YOLO model.…”

“…The integration architecture between the edge and the cloud is also necessary, as highlighted in the work of Chen et al in [74] and Loseto et al in [72]. The proposed distributed AI architecture allows local data training and model updates directly from the cloud server.…”

UHD Database Focus on Smart Cities and Smart Transport

“…With the help of this analysis, various situations have been studied, including traffic control and monitoring, security, and entertainment. In [31], the authors evaluate the transmission of multimedia data at a certain throughput setting. They then evaluate the performance and describe the benefits of real-time transfer.…”

Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving

“…An autonomous vehicle's essential task is object detection, which helps the vehicle to identify (classify) and estimate the other objects in the surrounding area [3], [17]. These objects can be other vehicles, pedestrians, traffic signs, signals, lane markings, and other roadside information [2], [38].…”

“…Connected Vehicles: In future autonomous vehicles, an object detection task can be performed collaboratively using distributed computing or joint training and inference [18], [20]. Methods proposed in this category include deploying AI models through edge computing, fog computing, cloud computing or their respective combinations [3], [22], [31], [33], [36]. As the AI model, such as DNN, can be dense in size and may require high computational resources, it is practically challenging to deploy them on Edge devices.…”