Non-linear kernel space invariant representation for view-invariant motion trajectory retrieval and classification

View-invariant representation has been shown to be a powerful tool in classification and retrieval of motion events due to camera motions. Traditional null space representation is invariant only for linear transformations and does not yield high accuracy for camera with non-linear motions. In this paper, we propose a novel general framework for non-linear kernel space invariant representation (NKSI), which is invariant to non-linear transformations due to camera motions with standard perspective transformation. We first derive NKSI and then propose an efficient classification and retrieval system relying on NKSI for archiving and searching motion events consisting of motion trajectories. The simulation results demonstrate superior performance of the proposed systems over traditional approaches.