Adaptive Label Allocation for Unsupervised Person Re-Identification

Yihu Song,Siyang Yu,Shuaishi Liu,Siyu Zhou

doi:10.3390/electronics11050763

Abstract

Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task.

Highlights

Person re-identification (Re-ID) has become a popular applications in the field of intelligent security and person tracking, which aims to match the same person appearing in different cameras
Some works in literature [8–12] focus on unsupervised domain adaption (UDA), which require the use of prior knowledge obtained from other labeled person data
A channel attention and transformer architecture (CATA) ablation experiment is conducted to prove the effectiveness of the CATA module

Summary

Introduction

Person re-identification (Re-ID) has become a popular applications in the field of intelligent security and person tracking, which aims to match the same person appearing in different cameras. Some works in literature [8–12] focus on unsupervised domain adaption (UDA), which require the use of prior knowledge obtained from other labeled person data. Most of the current works in literature [13–15] use clustering method to generate pseudo-labels for unsupervised person Re-ID training. This method appears to be very effective. There are many different pedestrians wearing similar clothes and appearing under the same camera which make them look very similar so that cluster-based methods will attribute them to the same class and assign the same pseudo-label

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Discussion

Conclusion