Abstract
We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.
Highlights
Visual surveillance is one of the major research areas in computer vision
We introduce the covariance matrix encoding the optical flow and intensity of each frame as the descriptor to represent the movement
Based on (20), we have an online implementation of the oneclass support vector machine (SVM) learning phase
Summary
Visual surveillance is one of the major research areas in computer vision. In a crowd image analysis problem, the scientific challenge includes abnormal events detection. Spatiotemporal motion features described by the context of bag of video words were adopted to detect abnormal events. In [11,12,13], spatiotemporal features modeled motion regions of the frame as background, and anomaly was detected by subtracting the newly sample to the background template. These works are similar to the change detection method when the background is not stable.
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