Table 2.

AUC of the abnormal event detection method based on covariance descriptors constructed by different features F via OC-SVM (Section 3.1), original LS-OC-SVM learning training samples together offline (Section 3.2), online LS-OC-SVM (Section 3.3) and sparse online LS -OC-SVM (Section 3.4). The biggest value of each method is shown in bold.

Features	Area under ROC
	lawn	indoor	plaza
offline OC-SVM
F1(6 × 6)	0.9474	0.8381	0.9148
F2(6 × 6)	0.9583	0.8410	0.9192
F3(8 × 8)	0.9656	0.8483	0.9367
F4(12 × 12)	0.9798	0.8744	0.9782
offline LS-OC-SVM
F1(6 × 6)	0.9755	0.8605	0.9422
F2(6 × 6)	0.9738	0.8603	0.9489
F3(8 × 8)	0.9788	0.8662	0.9538
F4(12 × 12)	0.9874	0.8900	0.9800
Online LS-OC-SVM
F1(6 × 6)	0.9755	0.8616	0.9403
F2(6 × 6)	0.9720	0.8730	0.9517
F3(8 × 8)	0.9795	0.8670	0.9563
F4(12 × 12)	0.9874	0.8904	0.9839
Sparse Online LS-OC-SV			M
F1(6 × 6)	0.8840	0.8077	0.9245
F2(6 × 6)	0.9435	0.8886	0.9515
F3(8 × 8)	0.9269	0.8266	0.9428
F4(12 × 12)	0.9510	0.8223	0.9501