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Person re-identification in UAV videos using relevance feedback

: Schumann, A.; Schuchert, Tobias

Postprint urn:nbn:de:0011-n-3331018 (3.0 MByte PDF)
MD5 Fingerprint: cb1f5d2766a73dbb5b67c0191387af60
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 21.4.2015

Loce, Robert P. (Ed.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Video surveillance and transportation imaging applications 2015 : 10–12 February 2015, San Francisco, California, United States
Bellingham, WA: SPIE, 2015 (Proceedings of SPIE 9407)
ISBN: 9781628414974
Paper 94070Z, 8 S.
Conference "Video Surveillance and Transportation Imaging Applications" <2015, San Francisco/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
aerial; appearance; feedback; person re-identification; retrieval; UAV

In this paper we present an approach to recognize individual persons in aerial video data. We start from a baseline approach that matches a number of color and texture image features in order to find possible matches to a query person track. This approach is improved by incorporating operator feedback in three ways. First, features are being weighted based on their degree of agreement (correlation) to the operator feedback. Second, a classifier is trained based on the operator feedback. This classifier learns to distinguish the query person from others. Third, we use feedback from past queries to further restrict the search space and improve feature selection as well as provide additional classifier training data. Finally we also investigate active learning strategies to select those results that the operator should label in order to gain the highest possible improvement of accuracy in the next iteration. We evaluate our approach on a publicly available dataset and demonstrate improvements over the baseline as well as over a previous work.