Abstract:

We develop an event detection framework that has two significant advantages over past work. First, we introduce an extended set of time-wise and object-wise statistical features including not only the trajectories but also histograms and HMM's of speed, orientation, location, size and aspect ratio. The proposed features are more expressive and enable detection of events that cannot be detected with trajectory-based features reported so far. Second, we introduce a spectral clustering method that can estimate the optimal number of clusters automatically. This novel clustering technique that is not adversely affected by high dimensionality. Unlike the conventional approaches that fit predefined models to events, we determine unusual events by analyzing the conformity scores. We compute affinity matrices and apply eignenvalue decomposition to find clusters to obtain the usual events. We prove that the number of clusters governs the number of eigenvectors used to span the feature similarity space. We also improve the feature selection process.