SCIENCE CHINA Information Sciences, Volume 63 , Issue 11 : 212102(2020) https://doi.org/10.1007/s11432-019-2811-8

## Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

• AcceptedJan 31, 2020
• PublishedOct 13, 2020
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### Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61762061, 62076117), National Key RD Program of China (Grant Nos. 2017YFB0801701, 2017YFB0802805), Natural Science Foundation of Jiangxi Province (Grant No. 20161ACB20004), and Jiangxi Key Laboratory of Smart City (Grant No. 20192BCD40- 002).

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• Figure 1

(Color online) The challenges associated with vehicle re-identification.

• Figure 2

(Color online) The overview of the proposed architecture for vehicle re-identification. Firstly, the dataset is input into the network. Then the discriminative fine-grained network part including the Siamese network in the upper part and the fine-grained network in the lower part is applied. Finally, the two-stage re-ranking part is executed to merge the feature vectors of the two sub-networks to obtain the final distance by the two-stage calculation.

• Figure 3

(Color online) The influence of the subtle feature information on vehicle re-identification.

• Figure 4

(Color online) Example of the selection of candidate $\overline{{p}}$ and the definition process of robust set ${H}^{*}({\rm~C},~20)$ in the second step of re-ranking.

• Figure 5

(Color online) CMC curves of different methods. (a) VeRi-776; (b) the small test set of VehicleID; (c) the medium test set of VehicleID; (d) the large test set of VehicleID.

• Table 1

Table 1Comparison of the state-of-the-art methods on the VeRi-776 dataset

 Method rank1 (%) mAP (%) LOMO [18] 24.59 9.68 FACT [9] 51.89 18.69 Siamese-Visual [11] 41.12 29.48 BOW-CN [16] 33.82 9.63 VAMI [38] 77.03 50.13 XVGAN [39] 60.20 24.65 DLCNN [26] 82.42 49.88 Ours 88.14 61.85
• Table 2

Table 2Comparison of the state-of-the-art methods on the VehicleID dataset

 Method Small Medium Large rank1 (%) rank5 (%) rank1 (%) rank5 (%) rank1 (%) rank5 (%) LOMO [18] 19.92 32.83 19.52 29.91 15.72 25.56 FACT [9] 49.93 68.37 45.01 64.75 40.12 60.59 VGG+CCL [32] 43.92 65.01 38.84 61.91 34.58 55.72 MixedDiff+CCL [32] 48.52 74.55 43.94 67.96 40.85 62.79 VAMI [38] 63.08 83.12 52.69 75.08 47.28 70.06 XVGAN [39] 52.79 80.69 49.47 71.42 44.92 66.71 DLCNN [26] 73.01 82.70 66.50 77.06 61.00 73.17 Ours 77.02 85.04 71.81 80.81 66.29 78.42
• Table 3

Table 3Comparison of the results obtained using the methods with and without re-ranking on VeRi-776

 Method rank1 (%) mAP (%) Base 88.14 61.85 Base+Zhong [43] 89.03 65.19 Base+TR 90.11 66.10
• Table 4

Table 4Comparison of the results obtained using the methods with and without re-ranking on VehicleID

 Method Small Medium Large rank1 (%) rank5 (%) rank1 (%) rank5 (%) rank1 (%) rank5 (%) Base 77.02 85.04 71.81 80.81 66.29 78.42 Base+Zhong [43] 77.89 85.28 72.38 81.06 67.92 79.17 Base+TR 79.00 86.01 74.06 82.19 69.50 79.79

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