Abstract
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.
Highlights
T HE day-to-day operational management of transport systems relies on large networks of sensors, actuators, and software to provide passengers with safe, reliable, and affordable means of transportation
We present a series of experiments that demonstrate the high passenger counting accuracy of Neural Automated Passenger Counting system (NAPC) on the previously introduced dataset
We introduced a real-time Neural Automated Passenger Counting system (NAPC), which is based on an end-to-end Long Short-Term Memory (LSTM) recurrent neural network
Summary
T HE day-to-day operational management of transport systems relies on large networks of sensors, actuators, and software to provide passengers with safe, reliable, and affordable means of transportation. They conducted experiments using a 7-segment bus monitoring video where the segments have different characteristics, such as dark/strong outside light, crowded while getting on/off, including passengers carrying children, including passengers with babies and children Their APC counted all 28 boarding passengers and 79 of 81 alighting passengers, resulting in an accuracy of 98%. Sun et al introduced a depth video stream generating the method from RGB-D videos obtained by a camera mounted on top of the door area of three different buses They propose a boarding and alighting passenger counting method combining a two-step (generating and refining head proposal) head detection with a tracking algorithm for the generated depth video samples [33]. Low-resolution depth images obtained by RGB-D sensors are used for privacy-preserving human pose estimation in [43] and for head detection in the task of counting boarding and alighting passengers [33]. Top-view depth images from a video surveillance system are employed for detecting people [7], as well as people committing attacks and intrusions [8]
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