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| Zitatschlüssel | Seidel2022 |
|---|---|
| Autor | Seidel, R. and Jahn, N. and Seo, S. and Goerttler, T. and Obermayer, K. |
| Seiten | 33-44 |
| Jahr | 2022 |
| DOI | 10.1109/OJITS.2021.3139393 |
| Journal | IEEE Open Journal of Intelligent Transportation Systems |
| Jahrgang | 3 |
| Zusammenfassung | Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set. |
| Typ der Publikation | Selected:structured selected:publications selected:main |
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