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Proceedings Paper

Feature selection for appearance-based vehicle tracking in geospatial video
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Paper Abstract

Current video tracking systems often employ a rich set of intensity, edge, texture, shape and object level features combined with descriptors for appearance modeling. This approach increases tracker robustness but is compu- tationally expensive for realtime applications and localization accuracy can be adversely affected by including distracting features in the feature fusion or object classification processes. This paper explores offline feature subset selection using a filter-based evaluation approach for video tracking to reduce the dimensionality of the feature space and to discover relevant representative lower dimensional subspaces for online tracking. We com- pare the performance of the exhaustive FOCUS algorithm to the sequential heuristic SFFS, SFS and RELIEF feature selection methods. Experiments show that using offline feature selection reduces computational complex- ity, improves feature fusion and is expected to translate into better online tracking performance. Overall SFFS and SFS perform very well, close to the optimum determined by FOCUS, but RELIEF does not work as well for feature selection in the context of appearance-based object tracking.

Paper Details

Date Published: 13 June 2013
PDF: 12 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 87470G (13 June 2013); doi: 10.1117/12.2015672
Show Author Affiliations
Mahdieh Poostchi, Univ. of Missouri-Columbia (United States)
Filiz Bunyak, Univ. of Missouri-Columbia (United States)
Kannappan Palaniappan, Univ. of Missouri-Columbia (United States)
Guna Seetharaman, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)

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