dc.contributor.author | Meli, Willson | |
dc.contributor.author | Lacy, Fred | |
dc.contributor.author | Ismail, Yasser | |
dc.date.accessioned | 2020-07-21T14:15:03Z | |
dc.date.available | 2020-07-21T14:15:03Z | |
dc.date.issued | 2020-11-01 | |
dc.identifier.issn | 2210-142X | |
dc.identifier.uri | https://journal.uob.edu.bh:443/handle/123456789/4036 | |
dc.description.abstract | Smart cities possess several technologies in collecting pedestrian activity data, which may be used to manage city planning. A growing body of research exists on video processing based pedestrian counting methods, due to the development of new computer vision techniques. This research reviews different, vision-based methods for counting pedestrians and applies a specific counting method which is formed by a combination of You Only Look Once Version 3 (YOLOv3) and Simple Online Real-time Tracking (SORT) with a deep association metric. The results suggest that although clustering, as well as the direction and intensity of pedestrian traffic, achieves a minimal effect on the count, occlusion constitutes the main source of errors. Adequate training may serve to increase accuracy. | en_US |
dc.language.iso | en | en_US |
dc.publisher | University of Bahrain | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Smart Cities | en_US |
dc.subject | YOLO Algorithm | en_US |
dc.subject | Pedestrians Counting Algorithm | en_US |
dc.subject | Simple Online Real-time Tracking | en_US |
dc.title | Video-Based Automated Pedestrians Counting Algorithms for Smart Cities | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://dx.doi.org/10.12785/ijcds/090605 | |
dc.volume | 9 | en_US |
dc.pagestart | 1065 | en_US |
dc.pageend | 1079 | en_US |
dc.source.title | International Journal of Computing and Digital Systems | en_US |
dc.abbreviatedsourcetitle | IJCDS | en_US |
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