Understanding People Flow in Transportation Hubs
This work addresses the need for reliable flow analysis in airport management, though it is incremental as it builds on existing clustering techniques with a new data-irregularity measure.
The paper tackled the problem of monitoring people flow in transportation hubs by proposing an unsupervised method to cluster flow patterns from 3D images, which successfully summarized representative patterns over 14 days in an airport and identified rare events like cleaning and security activities.
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).