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Pose-invariant vehicle identification in aerial electro-optical imagery

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Abstract

In digital airborne electro-optical imagery, the identification of objects, particularly vehicles, has an important role in wide-area search and surveillance applications. We propose an identification and pose estimation approach based on maximising the correlation of features in an image with projections of 3D models. It has been applied to imagery collected in a controlled laboratory environment as well as imagery collected during airborne field trials. The results show good discrimination between different vehicle classes, although performance is degraded by vehicle camouflage and low-resolution imagery. Our approach is scalable, in terms of database size and feature sets, and computationally efficient.

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Notes

  1. Although a Land Rover is not a generic vehicle class, a certain class of military vehicles consists of a majority of Land Rovers and hence the class label of the same name.

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Authors and Affiliations

  1. National Security, Intelligence and Reconnaissance Division (NSID), Defence Science and Technology Organisation (DSTO), Edinburgh, Australia

    Pranam Janney & David Booth

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  1. Pranam Janney
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  2. David Booth
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Correspondence to Pranam Janney.

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Janney, P., Booth, D. Pose-invariant vehicle identification in aerial electro-optical imagery. Machine Vision and Applications 26, 575–591 (2015). https://doi.org/10.1007/s00138-015-0687-9

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