| id |
acadia22_714 |
| authors |
Li, Yunqin; Zhang, Jiaxin; Wang, Xueqiang; Ma, Kai |
| year |
2022 |
| title |
Measuring Street Vitality Based on Video-image Using Deep Learning |
| source |
ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 714-725. |
| summary |
This paper proposes a deep convolutional neural network-based framework for fine-scale studies on automatic evaluation of street-level vitality using multiple object tracking and image segmentation with video data. A deep learning model for street vitality evaluation was proposed based on the intensity and complexity of pedestrian activities. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (11,546,572 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/02/06 14:04 |
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