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Paper Detail

Paper IDCOVID-IP-2.3
Paper Title DEEP PEDESTRIAN DENSITY ESTIMATION FOR SMART CITY MONITORING
Authors Kazuki Murayama, Kenji Kanai, Masaru Takeuchi, Heming Sun, Jiro Katto, Waseda University, Japan
SessionCOVID-IP-2: COVID Related Image Processing 2
LocationArea A
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recently, requirement of city monitoring and maintenance using ICT techniques increases with the help of transportation system. In addition, the spread of COVID-19 has increased the demand for managing pedestrian traffic volume. To contribute to these trends, in this paper, we propose a new pedestrian radar map system in order to estimate pedestrian density on streets and sidewalks. Our system uses e-bikes to collect 360-degree images and visualize pedestrian positions as a radar map. In evaluations, we confirm the accuracies of the radar maps and pedestrian density by using KITTI dataset and by carrying out a field experiment.