| Title |
Deep Pedestrian Density Estimation For Smart City Monitoring |
| ID_Doc |
39937 |
| Authors |
Murayama, K; Kanai, K; Takeuchi, M; Sun, HM; Katto, J |
| Title |
Deep Pedestrian Density Estimation For Smart City Monitoring |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/ICIP42928.2021.9506522 |
| 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. |
| Author Keywords |
density estimation; distance estimation; deep learning; mobile sensing |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000819455100047 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology |
| Research Area |
Computer Science; Engineering; Imaging Science & Photographic Technology |
| PDF |
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