1 research outputs found
Understanding People Flow in Transportation Hubs
In this paper, we aim to monitor the flow of people in large public
infrastructures. We propose an unsupervised methodology to cluster people flow
patterns into the most typical and meaningful configurations. By processing 3D
images from a network of depth cameras, we build a descriptor for the flow
pattern. We define a data-irregularity measure that assesses how well each
descriptor fits a data model. This allows us to rank flow patterns from highly
distinctive (outliers) to very common ones. By discarding outliers, we obtain
more reliable key configurations (classes). Synthetic experiments show that the
proposed method is superior to standard clustering methods. We applied it in an
operational scenario during 14 days in the X-ray screening area of an
international airport. Results show that our methodology is able to
successfully summarize the representative patterns for such a long observation
period, providing relevant information for airport management. Beyond regular
flows, our method identifies a set of rare events corresponding to uncommon
activities (cleaning, special security and circulating staff).Comment: 10 pages, 19 figure, 1 tabl