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Transferring attributes for person re-identification

: Schumann, A.; Stiefelhagen, R.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society; IEEE Computer Society; Karlsruher Institut für Technologie -KIT-:
12th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2015 : Karlsruhe, Germany, 25-28 August 2015; Including workshop papers
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-7633-4
ISBN: 978-1-4673-7632-7
International Conference on Advanced Video and Signal-Based Surveillance (AVSS) <12, 2015, Karlsruhe>
Fraunhofer IOSB ()

Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person’s appearance can help with some of these challenges. In this work we describe an approach that integrates such attributes with existing re-identification methods based on low-level features. We start by training a set of attribute classifiers and present a metric learning approach that uses these attributes for person re-identification. The method is then applied to a second dataset without attributes labels by transferring the attributes classifiers. Performance on the target dataset can be increased by applying a whitening transformation prior to transfer. We present experiments on publicly available datasets and demonstrate the performance improvement gained by this added re-identification cue.