@inproceedings{4c27d65c1eef4bbcb14a19913676a931,
title = "A low-cost iot platform for crowd density detection in Jakarta commuter line",
abstract = "The increasing number of Commuter Line passengers calls for innovation as to how the crowd density across the carriages of Commuter Line trains can be better distributed. We develop an IoT system to detect the crowd density of Commuter Line trains so that (incoming) passengers can be better informed regarding which carriage is best to get in, hence ameliorating the train density distribution. We investigate two different approaches for density detection: CNN and YOLO+KNN. Moreover, we also analyze the impact of different single-board computers, that is, Raspberry Pi 3B and NVIDIA Jetson Nano, and that of different camera angle settings. In total, there are 20 different scenario combinations. We comparatively evaluate the density detection performance as well as the business value for each scenario.",
keywords = "CNN, Commuter Line, Density Detection, Embedded System, IoT, KNN, YOLO",
author = "Umarghanis, {Syafiq Abdillah} and Fariz Darari and Ari Wibisono",
note = "Funding Information: This work is supported by the 2020 PUTI research grant {"}Knowledge Graph-based AI - Analysis and Applications{"} from Universitas Indonesia. We are grateful to FT. Kereta Commuter Indonesia for their support in data collection. We also thank the anonymous reviewers for their useful feedback. Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 ; Conference date: 17-10-2020 Through 18-10-2020",
year = "2020",
month = oct,
day = "17",
doi = "10.1109/ICACSIS51025.2020.9263180",
language = "English",
series = "2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "121--128",
booktitle = "2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020",
address = "United States",
}