Video analytics on the MLK Smart Corridor testbed

With the predicted boom of urban environment populations in the next 30 years, many new challenges in urban transportation will surface. In an effort to mitigate these, the Center for Urban Informatics and Progress (CUIP) has been introduced along with its testbed. One opportunity this testbed provides is the ability to utilize computer vision and video analytics to anonymously gather data on how citizens traverse the city. This thesis shall discuss an approach to real-time object tracking that serves as a basis for further analytics such as traffic flow data collection and near-miss detection. The proposed video analytics platform will aid citizens with their day-to-day commute through the corridor by deriving real-time data based on actual behavior seen in the citizens' commute. Furthermore, since the testbed is ever-expanding in both hardware and size the algorithms and software proposed in this thesis are designed to prioritize scalability.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Stovall, Jose

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu; Kandah, Farah
• College of Engineering and Computer Science

• Computer vision
• Traffic conflicts
• Traffic flow

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/655

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1818