Transferring Attributes for Person Re-Identification

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.