Improving Pedestrian Safety with Traffic Monitoring Camera and AI

In collaboration with the Austin Transportation Department, we study pedestrian road use and identify potential safety concerns by analyzing existing traffic camera videos. Our approach automatically analyzes the content of video data from existing traffic cameras using a semi-automated processing pipeline powered by the state-of-art computing hardware and algorithms. Transportation agencies often own extensive networks of monocular traffic cameras, which are typically used for traffic monitoring by officials and experts. While the information captured by these cameras can also be of great value in transportation planning and operations, such applications are less common due to the lack of scalable methods and tools for data processing and analysis. This project exemplifies how the value of existing traffic camera networks can be augmented using the latest computing techniques.

Team Members:
Weijia Xu (Texas Advanced Computing Center), Joel Myer (City of Austin), Natalia Ruiz (Centre of Transportation Research)

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