Anomaly detection in crowded scenes using log-Euclidean covariance matrix
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Funchal, Madeira, Portekiz, 27 - 29 Ocak 2018, cilt.4, ss.279-286, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 4
- Doi Numarası: 10.5220/0006618402790286
- Basıldığı Şehir: Funchal, Madeira
- Basıldığı Ülke: Portekiz
- Sayfa Sayıları: ss.279-286
- Anahtar Kelimeler: Anomaly detection, Log-Euclidean covariance matrices, One-class SVM, Video surveillance
- Hacettepe Üniversitesi Adresli: Evet
Özet
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.