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 reidentify 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 patch-based methods.
['Kang-Yu Ni', 'Terrell Nathan Mundhenk', 'Kyungnam Kim', 'Yuri Owechko']
Manifold-based fingerprinting for target identification