Vehicle re-identification based on local feature and focus fusion

Home > Archive>Volume 44, Issue 18, 2021 >167-174

Vehicle re-identification based on local feature and focus fusion
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                        Li HaoLi Hao
1 School?of?Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 2 School?of automation, Guangdong University of Technology, Guangzhou 510006,China
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Yang ChaoYang Chao
1 School?of?Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 2 School?of automation, Guangdong University of Technology, Guangzhou 510006,China
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Huang YouxinHuang Youxin
1 School?of?Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 2 School?of automation, Guangdong University of Technology, Guangzhou 510006,China
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Chen JiazheChen Jiazhe
1 School?of?Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 3 Experimental Teaching Department, Guangdong University of Technology, Guangzhou 510006,China
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Zhan RuidianZhan Ruidian
1 School?of?Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 2 School?of automation, Guangdong University of Technology, Guangzhou 510006,China
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Bao Hong2Bao Hong
School?of automation, Guangdong University of Technology, Guangzhou 510006,China
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Affiliation:1 School of Advanced Manufacturing, Guangdong University of Technology, Jieyang 515200,China; 2 School of automation, Guangdong University of Technology, Guangzhou 510006,China; 3 Experimental Teaching Department, Guangdong University of Technology, Guangzhou 510006,China
Clc Number:TP391.41
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Abstract:

There are many similar vehicles in city monitoring, which brings great challenges to vehicle re-identification. Local features such as front, window and roof are the subtle differences of similar vehicles. According to the attention characteristics of the thermal map of the vehicle detection algorithm, a MCRF-SSD algorithm is proposed to detect the local feature area of the vehicle, and combines it with GMM-EM clustering algorithm. The detection performance is better than the current mainstream algorithm on the open data set.At the same time, in order to increase the inter-instance and reduce the intra-instance, the Arcface loss function is introduced into the feature extraction stage. In order to improve the performance of vehicle re recognition, in the stage of global feature and local feature fusion, a focus fusion structure (FFS) method is proposed, which can preserve the spatial distribution of feature graph, and a learnable parameter is introduced to improve the efficiency of feature fusion. Experimental results show that the performance of the proposed algorithm is better than that of the current best performance scheme in public VehicleID and VeRi datasets.

Key words:Vehicle re-identification; Local features; Clustering algorithm; Feature extraction; Global features; Feature fusion; Focus fusion Focus fusion structure; Learnable parameter

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Online: August 09,2024
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