Real-Time Traffic and Road Surveillance With Parallel Edge Intelligence
Ruimin Ke, Chenxi Liu, Hao Yang, Wei Sun, Yinhai Wang
Abstract
Edge computing, which is an emerging concept to complement cloud computing, processes data closer to where the data is generated at the network edge. Transportation system is a critical component of our cities and is generating more and more data. To leverage the power of the increasing amount of data from traffic sensors, researchers have started exploring the feasibility of edge computing for traffic big data analytics. Given the resource constraints on edge devices, it is very challenging to achieve high intelligence and real-time capability at the same time. There are few edge transportation systems that function in a multi-tasking mode with high efficiency and intelligence on the edge. This paper proposes a multi-thread parallel edge system architecture and several lightweight algorithms for traffic and road surveillance. The designs ensure robustness and real-time capability for the key modules of the system.