Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Low resolution vehicle re-identification based on appearance features for wide area motion imagery

: Cormier, M.; Sommer, L.; Teutsch, Michael

Postprint urn:nbn:de:0011-n-4324656 (435 KByte PDF)
MD5 Fingerprint: 564b49977fb9555730587c92f3a25971
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 7.2.2017

Institute of Electrical and Electronics Engineers -IEEE-:
WACV 2016, IEEE Winter Applications of Computer Vision Workshops : March 7-9, 2016 in Lake Placid, New York, USA
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2115-4 (Print)
ISBN: 978-1-5090-2114-7 (Online)
ISBN: 978-1-5090-0642-7
Winter Conference on Applications of Computer Vision (WACV) <2016, Lake Placid/NY>
Workshop on Computer Vision Applications in Surveillance and Transportation <1, 2016, Lake Placid/NY>
Workshop on Automated Analysis of Video Data for Wildlife Surveillance <2, 2016, Lake Placid/NY>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

The description of vehicle appearance in Wide Area Motion Imagery (WAMI) data is challenging due to low resolution and renunciation of color. However, appearance information can effectively support multiple object tracking or queries in a real-time vehicle database. In this paper, we present a systematic evaluation of existing appearance descriptors that are applicable to low resolution vehicle reidentification in WAMI data. The problem is formulated as a one-to-many re-identification problem in a closed-set, where a query vehicle has to be found in a list of candidates that is ranked w.r.t. their matching similarity. For our evaluation we use a subset of the WPAFB 2009 dataset. Most promising results are achieved by a combined descriptor of Local Binary Patterns (LBP) and Local Variance Measure (VAR) applied to local grid cells of the image. Our results can be used to improve appearance based multiple object tracking algorithms and real-time vehicle database search algorithms.