MotionSearch: Context-Based Video Retrieval and Activity Recognition in Video Surveillance

Summary form only given. This talk will explore the use of motion information in video surveillance. We will focus primarily on retrieval and activity recognition of motion events in video and sensor databases characterized by multiple interactive motion trajectories. We will present a framework based on tensor decomposition for indexing and retrieval of multiple motion trajectories in video databases. An efficient method for extraction and insertion of partial information in video databases used for multiple motion trajectory representation based on high-order singular value decomposition of tensors will also be provided. We will present a solution to classification and activity recognition of multiple interactive motion trajectories by deriving an extension of hidden Markov models to multiple dimensions. A closed-form solution for the training and classification general forward-backward, expectation-maximization, and Viterbi algorithms for multiple dimensions will be provided for causal systems. An approach to multiple motion trajectory classification and activity recognition based on distributed multidimensional hidden Markov models will be presented for non-causal systems. We will finally introduce a new approach to view-invariance of motion trajectories for unknown and moving cameras based on a null-space matrix representation of motion trajectory information. We will also extend the null-space representation to tensors for view-invariant indexing and retrieval of multiple motion trajectories from unknown and moving cameras. A method for extraction and insertion of partial information into a video database of multiple motion trajectories represented based on null-space tensors is finally presented.