Global Selection and Local Differentiation Fusion for Vehicle Re-identification

doi:10.16182/j.issn1004731x.joss.23-1009

Abstract

Abstract:

To address the interference issues of different perspectives, complex backgrounds, and lighting intensity in vehicle re-identification caused by cross lens multi view differences, a vehicle re-identification network integrating global selection and local differentiation is proposed. Based on Resnet50 backbone network, a three-branch complementary network integrating global and local features is designed. The global branch is used to learn overall appearance information of the vehicle, while the local branch captures differential details of the vehicle. Based on attention mechanism, a context feature selection module (CFSM)is proposed to effectively separate vehicle information from complex background information, and a detail feature enhancement module (DFEM) is proposed to enhance the learning of multi granularity feature detail information by utilizing relative position information between components. A weight adaptive balancing strategyis proposed in combination with multiple loss functions for training. Experimental results show that on VeRi-776 dataset, the proposed network achieves 73.2%, 93.4% and 97.3% respectively on the mAP, CMC@1 and CMC@5. On a large-scale testing subset of VehicleID dataset, the proposed network achieves 75.0% and 92.7% respectively on the CMC@1 and CMC@5. Compared with the comparison network, the proposed network has higher recognition rate and robustness.

Key words: vehicle re-identification, multi-branch structure, global context features, local distinguishing features, weight adaptive strategy

CLC Number:

TP391.9

Xu Shengjun, Zhang Mengqian, Zhan Bohan, Liu Guanghui, Meng Yuebo. Global Selection and Local Differentiation Fusion for Vehicle Re-identification[J]. Journal of System Simulation, 2025, 37(1): 220-233.

Figures/Tables 15

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Optimal selection of weight adaptive strategies

Method(Re-Rank)	mAP	CMC@1	CMC@5
(a) 权值参数为固定值2:1	75.97	94.28	95.71
(b) MALM的自适应权重	75.70	93.80	95.65
(c) 标准差 $σ$ 为阈值点， $ω + β = 1$	76.58	94.70	95.83
(d) 平均值 $x ¯$ 为阈值点， $ω + β = 1$	76.33	94.10	95.53
(e) 标准差 $σ$ 为阈值点， $2 ω + β 1 = 2$ 且 $ω + β 2 = 1$	75.86	93.86	95.77
(f) 平均值 $x ¯$ 为阈值点， $2 ω + β 1 = 2$ 且 $ω + β 2 = 1$	76.17	94.22	95.59

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Method (Re-Rank)	CMC@5	CMC@10
(a) 竖直4，竖直2	94.82	96.60
(b) 竖直4	93.56	96.31
(c)竖直2	94.76	96.72
(d)竖直2，水平2	95.35	97.20
(e)竖直4，水平2	94.70	96.96
(f)竖直4，竖直4，水平2	94.82	96.66

Method (Re-Rank)	mAP	CMC@1	CMC@5
(a)单分支输出	72.44	91.84	94.22
(b)双分支输出	76.06	93.45	95.29

Method (Re-Rank)	mAP	CMC@1	CMC@5
Baseline	76.60	93.62	95.59
Baseline +CFSM	76.29	93.38	95.59
Baseline+CFSM +1*1Conv	76.86	94.10	96.01
Baseline + DFEM(=Lobal-1)	76.63	94.04	95.65
Baseline + DFEM (=Lobal-2)	76.74	93.80	95.77
Baseline + DFEM (=Lobal-1+ Lobal-2)	76.56	93.98	95.83
GSLD (ours)	77.17	94.28	96.35

Method(w/o Re-Rank)	mAP	CMC@1	CMC@5
EALN	57.4	84.4	94.1
RAM	61.5	88.6	94.0
AAVER	61.2	89.0	94.7
MTML	64.6	92.3	95.7
VANet	66.2	89.2	95.9
Batch sample	67.6	90.2	96.4
MSA	72.9	92.1	96.2
AM	71.1	93.5	97.1
GSLD (ours)	73.2	93.4	97.3

Method(w/o Re-Rank)	Test800		Test1600		Test2400
Method(w/o Re-Rank)	CMC@1	CMC@5	CMC@1	CMC@5	CMC@1	CMC@5
C2F-Rank	61.1	81.7	56.2	76.2	51.4	72.2
RAM	75.2	91.5	72.3	87.0	67.7	84.5
AAVER	74.7	93.8	68.6	90.0	63.5	85.6
PRN	78.4	92.3	75.0	88.3	74.2	86.4
MSA	77.6	90.5	74.4	86.3	72.9	84.6
URRNet	76.5	96.5	73.7	92.0	68.2	89.6
DSAM	80.6	94.8	78.2	92.6	75.0	89.2
GRMF	81.5	96.3	76.8	93.6	72.8	91.0
GSLD (ours)	82.6	97.8	77.5	94.8	75.0	92.7