Monte Carlo human identification refinement using joints uncertainty
Keywords:human re-identification, uncertainty of human joints, Monte Carlo method, RGB-D camera
In this work, we propose a new method to re-identify the same individual among different people using RGB-D data.
Each human signature is a combination of soft biometric traits. In particular, we extract a color-based descriptor and a local feature descriptor through a Monte Carlo-based algorithm taking into account the uncertainty of human joints and, applied to each descriptor, refines the similarity match against a spatiotemporal database that updates over time.
We analyzed the effects of Monte Carlo refinement in terms of the final maximum matching score obtained for the two descriptors. In addition, we tested the performance of the proposed method on a widely used public dataset against one of the best re-identification methods in the literature. Our method achieves an average recognition rate of 99.1 % rank-1 without identification error.
Its robustness also makes it suitable for industrial applications.
Copyright (c) 2023 Mariolino De Cecco, Alessandro Luchetti, Mattia Tavernini
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).