Person re-identification based on deep multi-view feature distance learning
DENG Xuan1, LIAO Kaiyang1,2, ZHENG Yuanlin1,3, YUAN Hui1, LEI Hao1, CHEN Bing1
1. College of Printing, Packaging Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an Shaanxi 710048, China; 2. Printing and Packaging Engineering Technology Research Centre of Shaanxi Province, Xi'an Shaanxi 710048, China; 3. Key Laboratory of Printing and Packaging Engineering of Shaanxi Province, Xi'an Shaanxi 710048, China
Abstract：The traditional handcrafted features rely heavily on the appearance characteristics of pedestrians and the deep convolution feature is a high-dimensional feature, so, it will consume a lot of time and memory when the feature is directly used to match the image. Moreover, features from higher levels are easily affected by human pose or background clutter. Aiming at these problems, a method based on deep multi-view feature distance learning was proposed. Firstly, a new feature to improve and integrate the convolution feature of the deep region was proposed. The convolution feature was processed by the sliding frame technique, and the integration feature of low-dimensional deep region with the dimension equal to the number of convolution layer channels was obtained. Secondly, from the perspectives of the deep regional integration feature and the handcrafted feature, a multi-view feature distance learning algorithm was proposed by utilizing the cross-view quadratic discriminant analysis method. Finally, the weighted fusion strategy was used to accomplish the collaboration between handcrafted features and deep convolution features. Experimental results show that the Rank1 value of the proposed method reaches 80.17% and 75.32% respectively on the Market-1501 and VIPeR datasets; under the new classification rules of CHUK03 dataset, the Rank1 value of the proposed method reaches 33.5%. The results show that the accuracy of pedestrian re-identification after distance-weighted fusion is significantly higher than that of the separate feature distance metric, and the effectiveness of the proposed deep region features and algorithm model are proved.
LIAO S, HU Y, ZHU X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:2197-2206.
[2]
WU S, CHEN Y-C, LI X, et al. An enhanced deep feature representation for person re-identification[C]//Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision. Washington, DC:IEEE Computer Society, 2016:1-8.
[3]
DING S, LIN L, WANG G, et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern Recognition, 2015, 48(10):2993-3003.
[4]
丁宗元,王洪元,陈付华,等.基于距离中心化与投影向量学习的行人重识别[J].计算机研究与发展,2017,54(8):1785-1794. (WANG Z Y, WANG H Y, CHEN F H, et al. Person re-identification based on distance centralization and projection vectors learning[J]. Journal of Computer Research and Development, 2017, 54(8):1785-1794.)
[5]
KÖSTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraints[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2012, 1:2288-2295.
[6]
FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]//Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2010:2360-2367.
[7]
张耿宁,王家宝,李阳,等.基于特征融合与核局部Fisher判别分析的行人重识别[J].计算机应用,2016,36(9):2597-2600. (ZHANG G N, WANG J B, LI Y, et al. Person re-identification based on feature fusion and kernel local Fisher discriminant analysis[J]. Journal of Computer Applications, 2016, 36(9):2597-2600.)
[8]
孙金玉,王洪元,张继,等.基于块稀疏表示的行人重识别方法[J].计算机应用,2018,38(2):448-453. (SUN J Y, WANG H Y, ZHANG J, et al. Person re-identification method based on block sparse representation[J]. Journal of Computer Applications, 2018, 38(2):448-453.)
[9]
TAO D, GUO Y, YU B, et al. Deep multi-view feature learning for person re-identification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(10):2657-2666.
[10]
GONG Y, WANG L, GUO R, et al. Multi-scale orderless pooling of deep convolutional activation features[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8695. Cham:Springer, 2014:392-407.
[11]
GRAY D, BRENNAN S, TAO H. Evaluating appearance models for recognition, reacquisition, and tracking[C]//Proceedings of the 2007 IEEE International Workshop on Performance Evaluation for Tracking and Surveillance. Piscataway, NJ:IEEE, 2007:41-49.
[12]
ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification:a benchmark[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2015:1116-1124.
[13]
LI W, ZHAO R, XIAO T, et al. DeepReID:deep filter pairing neural network for person re-identification[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:152-159.
[14]
LIU H, FENG J, QI M, et al. End-to-end comparative attention networks for person re-identification[J]. IEEE Transactions on Image Processing, 2017, 26(7):3492-3506.
[15]
ZHENG L, ZHANG H, SUN S, et al. Person re-identification in the wild[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2017:3346-3355.
[16]
ZHENG L, HUANG Y, LU H, et al. Pose invariant embedding for deep person re-identification[J]. arXiv E-print, 2018:arXiv:1701.07732.
[17]
ZHAO H, TIAN M, SUN S, et al. Spindle net:person re-identification with human body region guided feature decomposition and fusion[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2017:907-915.
[18]
PAISITKRIANGKRAI S, SHEN C, van den HENGEL A. Learning to rank in person re-identification with metric ensembles[C]//Proceedings of the 2015 Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:1846-1855.
[19]
MA B, SU Y, JURIE F. Covariance descriptor based on bio-inspired features for person re-identification and face verification[J]. Image and Vision Computing, 2014, 32(6/7):379-390
2019, Vol. 39 
