Machine learning has been widely used in discovering patterns from complex and unstructured data. Successful machine learning needs vast amounts of data and labels for training and validation. Creating datasets and labels require significant efforts. A team at Purdue University creates datasets using network cameras that can provide real-time visual data. These cameras can continuously stream live views of national parks, zoos, city halls, streets, university campuses, highways, shopping malls, and so on. The stationary cameras (some of them have PTZ, pan-tilt-zoom) have contextual information (such as time and location) about the visual data. By cross-referencing with other sources of data (such as weather and event calendar), it is possible to label the data automatically. The run-time system allocates and adjusts computing resources as needed. This system is a foundation for many research topics related to analyzing visual data, such as (1) whether today’s technologies are ready analyzing the versatile data, (2) what computing infrastructure is needed to handle the vast amount of real-time data, (3) where are the performance bottlenecks and how hardware accelerators (such as GPU) can improve performance, (4) how can this system automatically produce labels for machine learning.
Yung-Hsiang Lu is a 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 organizing committee of the IEEE Rebooting Computing Initiative. He is the lead organizer of Low-Power Image Recognition Challenge, the chair of the Multimedia Communication Systems Interest Group in IEEE Multimedia Communications Technical Committee. Dr. Lu and three Purdue students founded a technology company using video analytics to improve the operation of brick and mortar retail stores. The company’s cameras stream video to cloud for analysis and the analysis results help store owners serve customers better. This company receives a Small Business Innovation Research (SBIR-1) grant from the National Science Foundation in 2016. Dr. Lu obtained the Ph.D. from the Department of Electrical Engineering at Stanford University.