NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

• Published in: IEEE open journal of intelligent transportation systems Vol. 3; pp. 33 - 44
• Main Authors: Seidel, Robert, Jahn, Nico, Seo, Sambu, Goerttler, Thomas, Obermayer, Klaus
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Boarding; boarding and alighting passenger counting; Cameras; Convolutional neural networks; Cost function; Datasets; Feature extraction; Intelligent transportation; Laser radar; LiDAR; long short-term memory (LSTM); neural network; Neural networks; Passengers; Privacy; Public transportation; range imaging; Task analysis; Three-dimensional displays; Training
• ISSN: 2687-7813; 2687-7813
• DOI: 10.1109/OJITS.2021.3139393

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.