Crowd Modeling using Temporal Association Rules
Document Type
Conference Proceeding
Source of Publication
Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021
Publication Date
9-8-2021
Abstract
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.
DOI Link
ISBN
9781665401708
Publisher
IEEE
Disciplines
Computer Sciences
Keywords
Camera Calibration, Euclidean Path Modeling, Image Registration, Machine Vision
Scopus ID
Recommended Citation
Ahmed, Rizwan; Rafiq, Muhammad Shahzad; and Junejo, Imran N., "Crowd Modeling using Temporal Association Rules" (2021). All Works. 4659.
https://zuscholars.zu.ac.ae/works/4659
Indexed in Scopus
yes
Open Access
no