Opportunities and Challenges in Global Network Cameras
Associate Professor Yung-Hsiang Lu, School of Electrical and Computer Engineering and (by courtesy) the Department of Computer Science, Purdue University
Millions of network cameras have been deployed. Many of these cameras provide publicly available data, continuously streaming live views of national parks, city halls, streets, highways, and shopping malls.
A person may see multiple tourist attractions through these cameras, without leaving home. Researchers may observe the weather in different cities. Using the data, it is possible to observe natural disasters at a safe distance. News reporters may obtain instant views of an unfolding event. A spectator may watch a celebration parade from multiple locations using street cameras.
Despite the many promising applications, the opportunities of using global network cameras for creating multimedia content have not been fully exploited. The opportunities also bring forth many challenges. Managing the large amount of data would require fundamentally new thinking.
The data from network cameras are unstructured and have few metadata describing the content. Searching the relevant content would be a challenge. Because network cameras continuously produce data, processing must be able to handle the streaming data. This imposes stringent requirements of the performance.
In this presentation, I will share the experience of building a software system that aims to explore the opportunities using the data from global network cameras. CAM2 (Continuous Analysis of Many CAMeras) is a cloud-based system for studying the worldwide phenomena using network cameras. CAM2 provides an event-based API (application programming interface) and is open to researchers to analyse the data for their studies. The web interface allows users to select the cameras for analysis. The cloud computing engine can scale in response to the needs of analysis programs.
Yung-Hsiang Lu is an Associate Professor in the School of Electrical and Computer Engineering and (by courtesy) the Department of Computer Science of Purdue University. He is an ACM distinguished scientist and ACM distinguished speaker. He is a member in the organising committee of the IEEE Rebooting Computing Initiative. He is the lead organiser of the first Low-Power Image Recognition Challenge in 2015, the chair (2014-2016) of the Multimedia Communication Systems Interest Group in IEEE Multimedia Communications Technical Committee.
He obtained his Ph.D. from the Department of Electrical Engineering at Stanford University.
This project is supported in part by the US National Science Foundation grants 1535108, 1530914, 1427808, 0958487. Any opinions, findings, and conclusions or recommendations expressed in this presentation are those of the speaker and do not necessarily reflect the views of the National Science Foundation.