Journal
2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Volume -, Issue -, Pages 3806-3813Publisher
IEEE
DOI: 10.1109/IROS51168.2021.9636545
Keywords
-
Categories
Funding
- Institute of Information & communications Technology Planning & Evaluation(IITP) - Korea government(MSIT) [2020-0-00048]
Ask authors/readers for more resources
The paper presents an unsupervised vehicle Re-ID method that utilizes Self-supervised Metric Learning (SSML) based on a feature dictionary, eliminating the need for a labelled dataset. The approach combines dictionary-based positive label mining and dictionary-based triplet loss to enhance the discriminative features and refine the quality of results progressively.
The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative features from unlabelled vehicle images. Numerous methods using domain adaptation have achieved outstanding performance, but those methods still need a labelled dataset as a source domain. This paper addresses an unsupervised vehicle Re-ID method, which no need any types of a labelled dataset, through a Self-supervised Metric Learning (SSML) based on a feature dictionary. Our method initially extracts features from vehicle images and stores them in a dictionary. Thereafter, based on the dictionary, the proposed method conducts dictionary-based positive label mining (DPLM) to search for positive labels. Pair-wise similarity, relative-rank consistency, and adjacent feature distribution similarity are jointly considered to find images that may belong to the same vehicle of a given probe image. The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively. The iterative process with DPLM and DTL boosts the performance of unsupervised vehicle Re-ID. Experimental results demonstrate the effectiveness of the proposed method by producing promising vehicle Re-ID performance without a pre-labelled dataset. The source code for this paper is publicly available on https://github.com/andreYoo/VeRI_SSML_FD.git.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available