Evaluation of metric learning model for Person Re-identification

Abstract

The analysis of pedestrian tracking in videos is suitable for narrow scope measurements. On the other hand, Re-identification enables the expansion of the scope, thereby enhancing the method's practicality. While Re-identification commonly matches the same individual based on similarity using feature vectors generated by DNN models, the full impact of changes in input images on the similarity has not been fully understood. In this study, a dataset is created by capturing images of the same individuals under specific conditions to evaluate the factors influencing Re-identification. Furthermore, similarity between images in this dataset is measured using a Re-identification model. Based on these results, the influence of changes in input images on similarity is evaluated, and the characteristics of the model are clarified.