Combining Human Parsing with Analytical Feature Extraction and Ranking Schemes for High-Generalization Person Reidentification

Abstract

Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning (particularly Deep Learning (DL)) has become the main re-ID tool that has allowed to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization in respect of DL models. That is, models that are trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. In order to address this issue, we present a model without trainable parameters. This, in turn, results in a great potential for high generalization. This approach combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing wherein human parsing is used to obtain the initial subregion classification. We show that such combination, to a high extent, eliminates the drawbacks of existing analytical methods. In addition, we use interpretable color and texture features that have human-readable similarity measures associated with them. In order to verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets, thus achieving a competitive rank-1 accuracy comparable with that of DL models. Most importantly, we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks, while also being completely re-ID dataset agnostic. We also achieve a cross-domain mean average precision (mAP) that is higher than that of DL models in some experiments. Finally, we discuss the potential ways of adding new features to further improve the model. We also show the advantages of interpretable features for the purposes of constructing human-generated queries from verbal descriptions in order to conduct searches without a query image.