TY  - EJOU
AU  - Zhu, Meng 
AU  - Wang, Xingyue 
AU  - Ren, Honge 
AU  - Hakeem, Abeer 
AU  - Mohaisen, Linda 

TI  - Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted adaptively to mitigate the changing person posture issue. Secondly, we use a joint measurement method for the spatial-temporal information to fully harness the data, and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution problem. The experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading techniques. The proposed method shows superior outcomes on widely recognized benchmarks, which are the Market-1501, MSMT17, and DukeMTMC-reID datasets. Furthermore, the proposed algorithm attains a Rank-1 accuracy of 97.4% and 94.9% mAP (mean Average Precision) on the Market-1501 dataset. Moreover, it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.
KW  - Person reidentification; ReID; computer vision; self-attention; spatial-temporal information

DO  - 10.32604/cmc.2025.061421