Multitask Deep Learning for Edge Intelligence Video Surveillance System

Abstract

From the mutual empowerment of two high-speed development technologies: artificial intelligence and edge computing, we propose a tailored Edge Intelligent Video Surveillance (EIVS) system. It is a scalable edge computing architecture and uses multitask deep learning for relevant computer vision tasks. Due to the potential application of different surveillance devices are widely different, we adopt a smart IoT module to normalize the video data of different cameras, thus the EIVS system can conveniently found proper data for a specific task. In addition, the deep learning models can be deployed at every EIVS nodes, to make computer vision tasks on the normalized data. Meanwhile, due to the training and deploying of deep learning model are usually separated, for the related tasks in the same scenario, we propose to collaboratively train the depth learning models in a multitask paradigm on the cloud server. The simulation results on the publicly available datasets show that the system continuously supports intelligent monitoring tasks, has good scalability, and can improve performance through multitask learning.