View-invariant tensor null-space representation for multiple motion trajectory retrieval and classification

In this paper, we propose a novel general framework for tensor based null space affine invariants, namely, tensor null space invariants (TNSI) with a linear classifier for high order data classification and retrieval. We first derive TNSI, which is perfectly invariant to multidimensional affine transformations due to camera motions for multiple motion trajectories in consecutive motion events. We subsequently propose an efficient classification and retrieval system relying on TNSI for archiving and searching motion events consisting of multiple motion trajectories. The simulation results demonstrate superior performance of the proposed systems.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Xu Chen, Dan Schonfeld, Ashfaq Khokhar
• Keywords: ICASSP 2009, Multiple Motion Trajectories, Null Space, Null Space Invariants, Signal Processing