Journal of Shandong University(Engineering Science) ›› 2018, Vol. 48 ›› Issue (6): 67-73.doi: 10.6040/j.issn.1672-3961.0.2018.192

• Machine Learning & Data Mining • Previous Articles     Next Articles

Person re-identification based on random erasing pedestrian alignmentnetwork method

Cui JIN(),Hongyuan WANG*(),Shoubing CHEN   

  1. College of Information Science and Engineering, Changzhou University, Jiangsu 213164, Changzhou, China
  • Received:2018-07-18 Online:2018-12-20 Published:2018-12-26
  • Contact: Hongyuan WANG E-mail:807214072@qq.com;hywang@cczu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61572085)

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Abstract:

The detected pedestrian images were prone to misalignment and the depth network was prone to over-fitting phenomenon. Pedestrian datasets were preprocessed using pedestrian alignment networks and random erasing data enhancements. It made the images generating different levels of occlusion, and corrected the misalignment in the images by the spatial transformation network layer in the affine estimation branch. It cropped the large part of the background and filled in the missing part of the pedestrian images, which reduced the phenomenon of network over-fitting and improved the generalization ability of the network. The tests were performed on the Market1501, DuckMTMC-reID and CUHK03 datasets, which showed the value of rank-1 reached approximately 84%. Compared the methods of randomly erasing pedestrian alignment network with other methods, it was found that the test results of pedestrian recognition method for randomly erasing pedestrian alignment network were better.

Key words: person re-identification, pedestrian alignment network, data enhancement, overfitting, image misalignment

CLC Number: 

  • TP391

Fig.1

Preprocessing effect diagram"

Fig.2

Comparison of pedestrian images before and after aligning network"

Fig.3

Enhanced pedestrian alignment network diagram by random erasing data method"

Table 1

Experimental results of basic branch"

%
方法 Market1501 DukeMTMC-reID CUHK03(labeled) CUHK03(detected)
rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP
A 80.17 91.69 59.14 65.20 78.88 44.99 31.14 52.00 29.80 30.50 51.07 29.04
B 81.26 92.35 60.02 69.93 80.67 52.49 32.22 54.03 31.45 31.56 52.36 30.70

Table 2

Experimental results of alignment branch"

%
方法 Market1501 DukeMTMC-reID CUHK03(labeled) CUHK03(detected)
rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP
C 79.01 90.86 58.27 68.36 81.37 47.14 35.29 53.64 32.90 34.14 54.50 31.71
D 81.15 92.77 61.79 66.83 81.66 48.34 37.34 55.58 33.44 35.26 54.68 32.60

Table 3

Experimental results of random erasing pedestrian alignment network and pedestrian alignment network"

%
方法 Market1501 DukeMTMC-reID CUHK03(labeled) CUHK03(detected)
rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP rank-1 rank-5 mAP
E 82.81 93.53 63.35 68.36 81.37 51.51 36.86 56.86 35.03 36.29 55.51 34.05
F 84.17 95.34 65.79 71.50 81.66 55.02 38.77 58.69 37.23 38.46 57.73 36.20

Table 4

Experimental results comparison of different methodswith Market1501 dataset"

%
方法 rank-1 mAP
DADM 39.40 19.60
FisherNet 48.15 29.94
BoW+KISSME 44.42 20.76
ReRank 77.10 63.60
SVDnet 80.50 55.90
Transfer 83.70 65.50
GAN 83.97 66.10
Ours 84.17 65.79

Table 5

Experimental results comparison of differentmethods with DukeMTMC-reID dataset"

%
方法 rank-1 mAP
LOMO+XQDA 30.75 17.04
BoW+KISSME 25.13 12.17
GAN 67.68 47.13
APR 70.69 51.88
ReRank 71.06 52.40
SVDnet 71.32 53.90
Ours 71.50 55.02

Table 6

Experimental results comparison of differentmethods with CUHK03 dataset"

%
方法 labeled detected
rank-1 mAP rank-1 mAP
LOMO+XQDA 14.80 13.60 12.80 11.50
Resnet50+XQDA 32.00 29.60 31.10 28.20
ReRank 63.50 68.27 64.12 69.26
SVDnet 67.86 72.54 68.63 73.31
Transfer 84.10 84.20
Ours 38.77 37.23 38.46 36.00
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