Manifold-based fingerprinting for target identification

In this paper, we propose a fingerprint analysis algorithm based on using product manifolds to create robust signatures for individual targets in motion imagery. The purpose of target fingerprinting is to re-identify a target after it disappears and then reappears due to occlusions or out of camera view and to track targets persistently under camera handoff situations. The proposed method is statistics-based and has the benefit of being compact and invariant to viewpoint, rotation, and scaling. Moreover, it is a general framework and does not assume a particular type of objects to be identified. For improved robustness, we also propose a method to detect outliers of a statistical manifold formed from the training data of individual targets. Our experiments show that the proposed framework is more accurate in target reidentification than single-instance signatures and patchbased methods.

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Kang-Yu Ni, Terrell N. Mundhenk, Kyungnam Kim, Yuri Owechko
• Keywords: Computer Vision, Cvpr 2012, Fingerprint Analysis, Product Manifolds, Statistical Manifold