Stream Processing of Software-Defined Video Analytics on a Smart Campus

The demand for real-time video analytics is increasing due to the prosperity of surveillance cameras in smart spaces. In this extended abstract, we present our stream processing platform to dynamically deploy, manage, and upgrade software-defined video analytics. Our key innovation is to divide a smart space into multiple Internet-of-Things (IoT) zones. Each zone consists of a set of geo-collocated IoT devices, heterogeneous networks, and edge servers. By creating a hierarchical architecture of IoT zones, our distributed platform achieves better scalability and flexibility. We implement our stream processing platform using a set of opensource projects. We deploy it on a real testbed of eight smart street lamps on our campus, in which four surveillance cameras stream live videos to an edge server. The edge server runs multiple video analytics following the user demands, and the analytics results are stored in a time-series database for future usage. In this demo, we will demonstrate two sample video analytics: illegal parking detection and people counting. The conference attendees will get to navigate through our Web user interface for various operations, e.g., selecting surveillance cameras, rewinding to earlier date/time, and visualizing analytics results in plots. We believe our demo will stimulate constructive discussion at the conference venue in this emerging research area.