Abstract
The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.
Highlights
Visual surveillance is one of the major research areas in computer vision
In [21], an online one-class support vector machine (SVM) was presented following the idea of [22]: an exponential window was applied to the data to suit it to an adaptive scenario where the solution was able to track the changes of the data distribution and to forget old patterns
SVM proposed in Section 3.3 is summarized in Algorithm 1; the flowchart is shown in Figure 3 and explained below
Summary
Visual surveillance is one of the major research areas in computer vision. After recording events by a visual sensor, such as a camera, obtaining detailed information of individual or crowd behavior is a challenging object in this area; automatic abnormal event detection is required to provide convenience, safety and an efficient lifestyle for humanity [1]. The behavior was labeled as abnormal when the current motion pattern cannot be recognized as any state of the system or a particular sequence of states cannot be parsed with the stochastic model. These works relied on an explicit signal statistical model, and the abnormal events were the ones interpreted as statistical model abrupt changes, maximum likelihood or Bayesian estimation theory [7]. In [9], the irregular behavior of images and videos was detected by comparing the likelihood of patches via a probabilistic graphical model These methods based on separated patches, benefiting from the partial knowledge of the image, do not exploit the global information of the frame.
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