Dynamic Urban Surveillance Video Stream Processing Using Fog Computing

The recent rapid development of urbanization and Internet of things (IoT) encourages more and more research on Smart City in which computing devices are widely distributed and huge amount of dynamic real-time data are collected and processed. Although vast volume of dynamic data are available for extracting new living patterns and making urban plans, efficient data processing and instant decision making are still key issues, especially in emergency situations requesting quick response with low latency. Fog Computing, as the extension of Cloud Computing, enables the computing tasks accomplished directly at the edge of the network and is characterized as low latency and real time computing. However, it is non-trivial to coordinate highly heterogeneous Fog Computing nodes to function as a homogeneous platform. In this paper, taking urban traffic surveillance as a case study, a dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision making. Furthermore, we have explored the potential to enable multi-target tracking function using a simpler single target tracking algorithm. A prototype is built and the performance is evaluated. The experimental results show that our scheme is a promising solution for smart urban surveillance applications.