Title

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.

ISBN

9781665401708

Publisher

IEEE

Disciplines

Computer Sciences

Keywords

Camera Calibration, Euclidean Path Modeling, Image Registration, Machine Vision

Scopus ID

85118954971

Indexed in Scopus

yes

Open Access

no

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