Domain Adaptation for Person Re-Identification with Part Alignment and Progressive Pseudo-Labeling

Abstract

With the recent technological advances, surveillance cameras became accessible to the general public and a huge amount of nonstructured data is being gathered. However, extracting value from this data is challenging, especially for tasks that involve human images, such as face recognition and person re-identification. Annotation of this kind of data is a challenging and expensive task. In this work, we propose a domain adaptation workflow to allow CNNs that were trained in one domain to be applied to another domain without the need for annotated target data. Our method uses AlignedReID++ as the baseline, trained using a Triplet loss with batch hard. Domain adaptation is done in an unsupervised manner by clustering unlabeled data to generate pseudo-labels in the target domain. Our results show that domain adaptation really improves the performance of the CNN when applied in the target domain.