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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.

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Pedestrian detection is a high-profile topic in computer vision, in part because it has great relevance to autonomous driving and intelligent surveillance applications. However, most pedestrian detection algorithms perform stably only during the daytime with sufficient illumination. At night, there is still room for improvement and many challenges exist. These challenges include occlusion caused by objects or crowds, and the problem of image background segmentation caused by environments with varying illumination. In this paper, we propose a nighttime thermal image pedestrian detection system, which can be viewed as an extension of the Faster region-based convolutional neural network (R-CNN) method. The proposed system can be used for static surveillance scenarios. First, a part model branch is proposed to realize the learning of partial pedestrian block features. Second, a segmentation branch is incorporated to strengthen the positioning of the pedestrian foreground. Finally, the branches are integrated through the fused loss function to enable joint training and optimization of the detection model. To evaluate the performance of the proposed model, we tested the system with several nighttime surveillance scenes. The experimental results show that the proposed method can effectively deal with the occlusion problem under challenging illumination environments and achieve performance levels superior to those of some state-of-the-art deep-learning pedestrian detection methods.

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For Internet of Vehicles (IoV) applications, reliable autonomous driving systems usually perform the majority of their computations on the cloud due to the limited computing power of edge devices. The communication delay between cloud platforms and edge devices, however, can cause dangerous consequences, particularly for latency-sensitive object detection tasks. Object detection tasks are also vulnerable to significantly degraded model performance caused by unknown objects, which creates unsafe driving conditions. To address these problems, this study develops an orchestrated system that allows real-time object detection and incrementally learns unknown objects in a complex and dynamic environment. A YOLO-based object detection model in edge computing mode uses thermal images to detect objects accurately in poor lighting conditions. In addition, an attention mechanism improves the system’s performance without significantly increasing model complexity. An unknown object detector (UOD) automatically classifies and labels unknown objects without direct supervision on edge devices, while a roadside unit (RSU)-based mechanism is developed to update classes and ensure a secure driving experience for autonomous vehicles. Moreover, the interactions between edge devices, RSU servers, and the cloud are designed to allow efficient collaboration. The experimental results indicate that the proposed system learns uncategorized objects dynamically and detects instances accurately.