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Date

11-9-2021

Description

Intelligent transportation systems (ITS) change our communities by improving the safety and convenience of people’s daily mobility. The system relies on multimodal traffic monitoring, that needs to provide reliable, efficient and detailed traffic information for traffic safety and planning. How to reliably and intelligently monitor intersection traffic with multimodal information is one of the most critical topics in intelligent transportation research. In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people’s daily mobility in terms of safety and convenience. In this study, we use a high-resolution millimeter-wave (mmWave) radar sensor to obtain a relatively richer radar point cloud representation for a traffic monitoring scenario. Based on a new feature vector, we use the multivariate Gaussian mixture model (GMM) to do the radar point cloud segmentation, i.e. ‘pointwise’ classification, in an unsupervised learning environment. In our experiment, we collected radar point clouds for pedestrians and cars, which also contained the inevitable clutter from the surroundings. The experimental results using GMM on the new feature vector demonstrated a good segmentation performance in terms of the intersection-over-union (IoU) metrics. The detailed methodology and validation metrics are presented and discussed.

Biographical

Siyang Cao joined the University of Arizona in 2015 as assistant professor of electrical and computer engineering, following a position as an automotive radar system engineer on algorithm, software and hardware development at Delphi. Cao is a graduate of The Ohio State University. His research focuses on the areas of radar signal processing, electronically scanned radar systems, radar imaging and machine learning with an emphasis on radar applications.

Subjects

Signalized intersections -- Research, Intelligent sensors, Traffic monitoring, Safety measures -- Technological innovations, Intelligent transportation systems

Disciplines

Transportation | Urban Studies

Persistent Identifier

https://archives.pdx.edu/ds/psu/37678

Webinar: Radar Point Cloud Segmentation using GMM in Traffic Monitoring

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