Professors Work to Advance Digital Signage through Deep Learning

Texas A&M University-Commerce Assistant Professor of Computer Science, Dr. Mingon Kang began a project to revolutionize digital signage with co-principal investigator Professor and Department Head of the Department of Computer Science & Information Systems, Dr. Sang Suh.

“Digital signage is facing a new era of intelligent systems. While digital signage solutions traditionally provide one-way communication for advertisement, intelligent systems as a next generation of the digital signage support multiple types of communication methods,” said Kang.

The interactive intelligent systems will collect statistic information about people viewing the digital signs, which will allow the sign to adjust according to the approximate age and gender of the viewers through Deep Learning, a branch of machine learning related to creating artificial intelligence within the device.

With this advanced interactive signage, advertisers will be able to tailor messages to more specific audiences. The digital signage devices may be able to track the movements of viewers then predict the consumer's behavior and communicate that prediction with another digital signage device. The predictive systems required for the digital signs may work through machine learning techniques including recommendation algorithms, a social network of digital devices and collective intelligent systems.

In addition, data sets too large for commonplace processing applications, big data, will be collected from the digital signage devices and analyzed to provide meaningful data that describes other data, metadata. The metadata developed from big data will help the signs function properly and provide valuable scientific information for further research in economics and business.

“The ultimate goal of the project is to involve students in cutting-edge research so that the students can develop research careers,” said Kang. “Furthermore, I aim to make close relationships between Texas A&M University-Commerce and up-and-coming startup companies.”

Along with helping develop recognition methods for age and gender based on Deep Learning, analyze big data for consumer patterns and research related to economics and business, a seed fund has allowed computer science master's student Dhiraj Gharana to work as a research assistant on the project.

For more information about computer science, visit the Department of Computer Science & Information Systems.