Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 91-97.doi: 10.6040/j.issn.1672-3961.0.2018.347

• Machine Learning & Data Mining • Previous Articles     Next Articles

Pedestrian recognition based on singular value decomposition pedestrian alignment network

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

  1. School of Information Science and Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
  • Received:2018-08-14 Online:2019-10-20 Published:2019-10-18
  • Contact: Hongyuan WANG E-mail:27106976@qq.com;hywang@cczu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61572085);国家自然科学基金资助项目(61806026);国家自然科学基金资助项目(61806026)

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Abstract:

In order to solve the problem that the background of pedestrian image was too large and the part of pedestrian was missing in the training data set of pedestrian recognition, the spatial transformation network layer was used to process the image dislocation. In order to optimize the deep learning process of the whole network and improve the image retrieval ability, a feature layer was added to the network, and singular value vector decomposition was used to process it. By combining the pedestrian alignment network with the singular vector decomposition and constructing the singular value decomposition pedestrian alignment network, the image dislocation problem could be solved and the effect of similarity measurement of image features could be improved. Experiments were conducted on Market1501, CUHK03 and DukeMTMC-reID datasets, and compared with pedestrian alignment network and other pedestrian re-recognition methods of deep learning and non-deep learning. In the experimental results, the values of rank-1 and mAP mean average precision reached 80% and 65% on average, which indicated that singular value decomposition of pedestrian alignment network had certain benefits on pedestrian matching effect.

Key words: pedestrian recognition, singular vector decomposition, pedestrian alignment, deep learning, spatial transformation network

CLC Number: 

  • TP391

Fig.1

Singular value decomposition pedestrian alignment network framework"

Table 1

Experimental results of rank-1 and mAP in different datasets"

网络 方法 Market1501数据集 DukeMTMC-reID数据集 CUHK03(labeled)数据集 CUHK03(detected)数据集
rank-1/% mAP/% rank-1/% mAP/% rank-1/% mAP/% rank-1/% mAP/%
第1条分支 PAN 80.17 59.14 65.2 44.99 31.14 29.80 30.50 29.04
ours 81.38 62.02 66.74 45.96 79.19 78.82 77.06 80.29
第3条分支 PAN 79.01 58.27 69.36 47.14 35.29 32.90 34.14 31.71
ours 80.17 60.85 69.07 48.86 78.55 79.12 78.68 81.41
整个网络 PAN 82.81 63.35 68.36 51.51 36.86 35.03 36.29 34.05
ours 83.41 65.09 75.54 52.20 79.63 80.45 78.35 81.22

Table 2

Experimental results comparison of rank-1 in SVDNet and singular value decomposition pedestrian alignment network"

%
方法 Market1501 DuckMTMC-reID CUHK03(labeled) CUHK03(detected)
SVDNet 82.3 80.05 81.80 76.70
ours 83.41 75.54 79.63 78.35

Table 3

Experiment results comparison of rank-1(%) in PAN and SVDNet"

%
方法 Market1501 DuckMTMC-reID CUHK03(labeled) CUHK03(detected)
PAN 82.81 80.05 36.86 36.29
ours 83.41 75.54 79.63 78.35

Table 4

Experimental results comparison of rank-1 and mAP in different methods on Market1501 and CUHK03 datasets"

%
方法 Market1501 CUHK03
rank-1 mAP rank-1 mAP
LOMO+XQDA 43.80 22.20 44.60 51.50
Siamese LSTM 61.60 35.30 57.30 46.30
Gated SCNN 65.90 39.60 61.80 51.30
DLCE 79.50 59.90 79.63 80.45
ours 83.41 65.09 79.52 81.34

Table 5

Experimental results comparison of rank-1 and mAP in different methods on DukeMTMC-reID dataset"

%
方法 rank-1 mAP
Bow+kissme 25.1 12.2
LOMO+XQDA 30.8 17
Baseline 65.5 44.1
GAN 67.7 47.1
ours 75.54 52.2
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