Abnormal event detection via multi-instance dictionary learning
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Local Upsampling Fourier Transform for accurate 2D/3D image registration
Computers and Electrical Engineering
Video manifold modelling: finding the right parameter settings for anomaly detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Computer Vision and Image Understanding
Online detection of abnormal events in video streams
Journal of Electrical and Computer Engineering
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In this paper, we proposed a novel real-time abnormal event detection framework that requires a short training period and has a fast processing speed. Our approach is based on phase correlation and our newly developed spatial-temporal co-occurrence Gaussian mixture models (STCOG)with the following steps: (i) a frame is divided into non-overlapping local regions; (ii) phase correlation is used to estimate the motion vectors between successive two frames for all corresponding local regions, and (iii) STCOG is used to model normal events and detect abnormal events if any deviation from the trained STCOG is found. Our proposed approach is also able to update the parameters incrementally and can be applied in complicated scenes. The proposed approach outperforms previous ones in terms of shorter training periods and lower computational complexity.