https://scholars.lib.ntu.edu.tw/handle/123456789/597889
標題: | Measuring In-Building Spatial-Temporal Human Distribution through Monocular Image Data Considering Deep Learning-Based Image Depth Estimation | 作者: | Qiu W.-X JEN-YU HAN ALBERT CHEN |
關鍵字: | Automated external defibrillator (AED);Clustering;Coordinates projection;Deep learning;Demand estimation;Depth estimation;Human counting;Image analysis;Indoor surveillance;Object tracking;Buildings;Cameras;Image recording;Object detection;Object recognition;Camera parameter;Detection and tracking;Image coordinates;In-building network;Monocular image;Object detection and tracking;Spatial temporals;Spatial-temporal distribution | 公開日期: | 2021 | 卷: | 35 | 期: | 5 | 來源出版物: | Journal of Computing in Civil Engineering | 摘要: | This research estimated the spatial-temporal distribution of humans in buildings through image sensing. Inputs were the in-building network, image sequences recording the movement of human, and camera parameters. Object detection and tracking models were utilized to discover humans in the images. Image depth estimation, clustering, and the camera model were integrated for the association of human and the in-building space in the image coordinates with the real world coordinates. The temporal human count for each in-building space was acquired. To validate the approach, two real cases in a school building, at a corridor and a hallway, were tested, and a synthesized case was carried out to exclude error from the detection and tracking steps. The proposed approach achieved results comparable to those of manual counting. ? 2021 American Society of Civil Engineers. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109812879&doi=10.1061%2f%28ASCE%29CP.1943-5487.0000976&partnerID=40&md5=8de084c1250a951ebed6e97976e50ef6 https://scholars.lib.ntu.edu.tw/handle/123456789/597889 |
ISSN: | 08873801 | DOI: | 10.1061/(ASCE)CP.1943-5487.0000976 |
顯示於: | 土木工程學系 |
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