Intelligent Video Analytics for Better Planning of Smart Cities using Edge Computing

Recent years have shown how machine learning and deep learning techniques can be used to radically transform smart cities. However, current cloud-centric processing approaches present latency, privacy, and bandwidth-related challenges. Edge computing is a solution to the above challenges where the video feeds captured can be analyzed right at the source of generation of the feed while deeper insights and analytics can still be performed on the cloud. The edge computing approach to video analytics also enables us to take action to certain situations in real-time which is not possible in cloud-centric approaches. In this paper, we propose certain methods for better planning of smart cities using edge computing. The data points generated for the proposed methods are based on 2 use cases - traffic counting using Nvidia DetectNet and pedestrian face mask detection using MobileNetV2. We also discuss how video frame resizing can be used for enhanced processing on the edge device.