Video Anomaly Detection based on ULGP-OF Descriptor and One-class ELM

Smart video analysis is attracting increasing attention with the pervasive use of surveillance camera. In this paper, we address video anomaly detection by Uniform Local Gradient Pattern based Optical Flow (ULGP-OF) descriptor and one-class extreme learning machine (OCELM). Using the proposed ULGP-OF descriptor, we naturally combine the robust 2D image texture descriptor LGP with video optical flow to jointly descibe the texture and motion characteristics of video. ULGP-OF significantly outperforms other frequently-used classic video decriptors by a 6% to 10% EER reduction. As to normal video event modeling, the newly emergent ELM is introduced for the first time to tackle the unbearable training time incurred by massive training data from video streams. Compared to classic data description algorithms like one-class SVM (OCSVM) and sparse coding, OCELM can yield competitive results with a significant improvement in learning speed, which makes our approach more applicable to large-scale video analysis and easier for updating when video data are explosively generated in this day and age. Moreover, by adopting consistency-based criteria, only one parameter needs to be appointed for OCELM before training, which renders our approach much more parameter-free than other anomaly detection techniques like sparse coding. Experiments on UCSD ped1 and ped2 datasets demonstrate the effectiveness of our approach.