A Generated Multi Branch Feature Fusion Model for Vehicle Re-identification

• Autor(a) principal: Zhijun,Hu
• Data de Publicação: 2021
• Outros Autores: Raj,Raja Soosaimarian Peter, Lilei,Sun, Lian,Wu, Xianjing,Cheng
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Archives of Biology and Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132021000100617
• topic: vehicle re-identification; deep learning; feature fusion; correlation coefficient matrix; global feature

Abstract Vehicle re-id play a very import role in recent public safety, it has received more and more attention. The local features (e.g. hanging decorations and stickers) are widely used for vehicle re-id, but the same local feature exists in one perspective, but not exactly exists in other perspectives. In this paper, we firstly use experiments to verify that there is a low linear correlation between different dimension global features. Then we propose a new technique which uses global features instead of local features to distinguish the nuances between different vehicles. We design a vehicle re-identification method named a generated multi branch feature fusion method (GMBFF) to make full use of the complementarity between global features with different dimensions. All branches of the proposed GMBFF model are derived from the same model and there are only slight differences among those branches. Each of those branches can extract highly discriminative features with different dimensions. Finally, we fuse the features extracted by these branches. Existing research uses the fusing features for fusion and we use the global vehicle features for fusion. We also propose two different feature fusion methods which are single fusion method (SFM) and multi fusion method (MFM). In SFM, features for fusion with larger dimension occupy more weight in fused features. MFM overcomes the disadvantage of SFM. Finally, we carry out a lot of experiments on two widely used datasets which are VeRi-776 dataset and Vehicle ID dataset. The experimental results show that our proposed method is much better than the state-of-the-art vehicle re-identification methods.