Crowd Modeling using Temporal Association Rules
Source of Publication
Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021
Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen's interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data.
Camera Calibration, Euclidean Path Modeling, Image Registration, Machine Vision
Ahmed, Rizwan; Rafiq, Muhammad Shahzad; and Junejo, Imran N., "Crowd Modeling using Temporal Association Rules" (2021). All Works. 4659.
Indexed in Scopus