Abstract
This paper proposes a new method to detect abnormality in a video captured for surveillance applications in real-time and also overcome the problem of the curse of dimensionality. To extract features related to any change in the video, nonlinear Gaussian fuzzy lattice functions have been applied on each frame of the video which results in the formation of fuzzy lattices. These fuzzy lattices have been expressed in the form of Schrodinger equation to find the kinetic energy involved corresponding to any change in the video. A number of the fuzzy lattice has been used as a dimension of the feature. It reduces the dimensionality significantly as compared to other state-of-the-art methods. Finally, the kinetic energy parameter is classified into normal and abnormal activities with the help of SVM-based classifier.
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