Pedestrian monitoring techniques for crowd-flow prediction
Smart Infrastructure and Construction
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Martani, C., Stent, S., Acikgoz, S., Soga, K., Bain, D., & Jin, Y. (2017). Pedestrian monitoring techniques for crowd-flow prediction. Smart Infrastructure and Construction, 170 (2. 1700001), 17-27. https://doi.org/10.1680/jsmic.17.00001
The high concentration and flow rate of people in train stations during rush hours can pose a prominent risk to passenger safety and comfort. In situ counting systems are a critical element for predicting pedestrian flows in real time, and their capabilities must be rigorously tested in live environments. The focus of this paper is on evaluating the reliability of two alternative counting systems, the first using an array of infrared depth sensors and the second a visible light (RGB) camera. Both proposed systems were installed at a busy walkway in London Bridge station. The data were collected over a period of 2 months, after which, portions of the data set were labelled for quantitative evaluation against ground truth. In this paper, the implementation of the two different counting technologies is described, and the accuracy and limitations of both approaches under different conditions are discussed. The results show that the developed RGB-based system performs reliably across a wide range of conditions, while the depth-based approach proves to be a useful complement in conditions without significant ambient sunlight, such as underground passageways.
Is supplemented by: https://doi.org/10.1680/jsmic.17.00001
External DOI: https://doi.org/10.1680/jsmic.17.00001
This record's URL: https://www.repository.cam.ac.uk/handle/1810/264688