Authors:
Efsun Sefa Sezer
and
Ahmet Burak Can
Affiliation:
Hacettepe University, Turkey
Keyword(s):
Anomaly Detection, Video Surveillance, Log-Euclidean Covariance Matrices, One-class SVM.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
In this paper, we propose an approach for anomaly detection in crowded scenes. For this purpose, two important
types of features that encode motion and appearance cues are combined with the help of covariance matrix.
Covariance matrices are symmetric positive definite (SPD) matrices which lie in the Riemannian manifold and
are not suitable for Euclidean operations. To make covariance matrices suitable for use in the Euclidean space,
they are converted to log-Euclidean covariance matrices (LECM) by using log-Euclidean framework. Then
LECM features created in two different ways are used with one-class SVM to detect abnormal events. Experiments
carried out on an anomaly detection benchmark dataset and comparison made with previous studies
show that successful results are obtained.