Person re-identification (ReID) is a critical work in the field of intelligent image processing and deep learning, which has attracted the attention of industry application. Person ReID focuses on matching person images obtained from non-overlapping camera views and finding the person-of-interest. An important unresolved problem is to obtain efficient metric for measuring the similarity among pedestrian images. Lately, deep learning with metric learning has become a general method for person ReID. Yet, previous methods mainly used a variety of distance to measure the similarity among samples. The way of distance measure is more sensitive when the scale changes. In this paper, we propose angular loss with hard sample mining (ALHSM) to learn better similarity metric for the person ReID. Our work uses the angular relationship in triangles as a measure of similarity, minimizing the angle at the negative point of the triangle. ALHSM combines with hard negative mining strategies, which learn better similarity metric and achieve advanced performance on several benchmark datasets. The experimental results show that our work is competitive compared to the state-of-the-art.
W.Li, R.Zhao, T.Xiao and X.Wang, Deepreid: Deep filter pairing neural network for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition9(24) (2014), 152–159.
2.
Q.Xiao, H.Luo and C.Zhang, Margin sample mining loss: A deep learning based method for person re-identification.arXiv preprint arXiv:1710.00478, (2017).
3.
Y.Wang, Z.Wang, W.Jia, X.He and M.Jiang, Joint learning of body and part representation for person re-identification, IEEE Access6(8) (2018), 44199–44210.
4.
Z.Zheng, L.Zheng and Y.Yang, A discriminatively learned cnn embedding for person reidentification, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)14(1) (2017), 13–21.
5.
D.Cheng, Y.Gong, S.Zhou, J.Wang and N.Zheng, Person re-identification by multi-channel parts-based cnn with improved triplet loss function. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1335–1344.
6.
L.Zheng, Y.Yang and A.G.Hauptmann, Person reidentification: Past, present and future.arXiv preprint arXiv:1610.02984, (2016).
7.
H.Song, Y.Xiang, S.Jegelka and S.Savarese, Deep metric learning via lifted structured feature embedding. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 4004–4012.
8.
A.Hermans, L.Beyer and B.Leibe, In defense of the triplet loss for person re-identification.arXiv preprint arXiv:1703.07737, (2017).
9.
W.Chen, X.Chen, J.Zhang and K.Huang, A multi-task deep network for person re-identification. in Association for the Advance of Artificial Intelligence1(2) (2017), 3988–3994.
10.
T.Ni, X.Gu, H.Wang, Z.Zhang, S.Chen and C.Jin, Discriminative deep transfer metric learning for cross-scenario person re-identification, Journal of Electronic Imaging27(4) (2018), 26–43.
11.
W.Li, X.Zhu and S.Gong, Person re-identification by deep joint learning of multi-loss classification. in Proceedings of International Joint Conference on Artificial Intelligence8(19) (2017), 2194–2200.
12.
W.Chen, X.Chen, J.Zhang and K.Huang, Beyond triplet loss: A deep quadruplet network for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition11(6) (2017), 403–412.
13.
K.Sohn, Improved deep metric learning with multi-class N-pair loss objective. in Advances in Neural Information Processing Systems12(5) (2016), 1857–1865.
14.
E.Ustinova and V.S.Lempitsky, Learning deep embeddings with histogram loss. in Advances in Neural Information Processing Systems12(5) (2016), 4177–4185.
15.
P.Naik and Dr. A.Rama Mohan Reddy, Optimized Configuration for OSPF Protocol to Aggregate Routes using Prim’s
MST Algorithm, International Journal of Innovative Research in Computer and Communication Engineering
(IJIRCCE)6(8), 2018.
16.
L.Zheng, L.Shen, L.Tian, S.Wang, J.Wang and Q.Tian, Scalable person re-identification: A benchmark. in Proceedings of the IEEE International Conference on Computer Vision2(17) (2015), 1116–1124.
17.
L.Zheng, Z.Bie, Y.Sun, J.Wang, C.Su and S.Wang, MARS: A video benchmark for large-scale person re-identification. in Proceedings European Conference on Computer Vision99(10) (2016), 868–884.
18.
M.Koestinger, M.Hirzer, P.Wohlhart, P.M.Roth and H.Bischof, Large scale metric learning from equivalence constraints. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition6(16) (2012), 2288–2295.
19.
W.Liao, M.Ying Yang, N.Zhan and B.Rosenhahn, Triplet-based deep similarity learning for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition1(19) (2017), 385–393.
20.
S.Paisitkriangkrai, C.Shen and A.van den Hengel, Learning to rank in person re-identification with metric ensembles. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition10(14) (2015), 1846–1855.
21.
F.Schroff, D.Kalenichenko and J.Philbin, FaceNet: A unified embedding for face recognition and clustering
inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition10(14) (2015),
815–823.
22.
L.Wu, C.Shen and A.van den Hengel, Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification, Pattern Recognition65(5) (2017), 238–250.
23.
S.Ding, L.Lin, G.Wang and H.Chao, Deep feature learning with relative distance comparison for person re-identification, Pattern Recognition48(10) (2015), 2993–3003.
24.
A.Fix, A.Gruber, E.Boros and R.Zabih, A graph cut algorithm for higher-order markov random fields. in Proceedings of the IEEE International Conference on Computer Vision11(6) (2011), 1020–1027.
25.
K.Kundu, Techniques Used for Mining Data in Educational System, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE)6(6) 2018.
26.
O.Duchenne, F.R.Bach, I.Kweon and J.Ponce, A tensor-based algorithm for high-order graph matching, IEEE Trans Pattern Anal Mach Intell33(12) (2011), 2383–2395.
27.
J.Wang, F.Zhou, S.Wen, X.Liu and Y.Lin, Deep metric learning with angular loss, In International Conference on Computer Vision and Pattern Recognition12(22) (2017), 2593–2601.
28.
F.Wang, W.Zuo, L.Lin, D.Zhang and L.Zhang, Joint learning of single-image and cross-image representations for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1288–1296.
29.
T.Oda and L.Tetsuya, A vegetable category recognition system: A comparison study for caffe and Chainer DNN frameworks, Soft Computing23(9) (2019), 3129–3136.
30.
D.Chen, Z.Yuan, B.Chen and N.Zheng, Similarity learning with spatial constraints for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1268–1277.
31.
L.Zhang, T.Xiang and S.Gong, Learning a discriminative null space for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1239–1248.
32.
L.Wu, C.Shen and A.van den Hengel, Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification, Pattern Recognition65(5) (2016), 238–250.
33.
E.Ustinova, Y.Ganin and V.Lempitsky, Multi-region bilinear convolutional neural networks for person re-identification. in Advanced Video and Signal Based Surveillance10(20) (2017), 1–6.
34.
I.B.Barbosa, M.Cristani, B.Caputo, A.Rognhaugen and T.Theoharis, Looking beyond appearances: Synthetic training data for deep cnns in re-identification, Computer Vision and Image Understanding167(2) (2018), 50–62.
35.
H.Zhao, M.Tian, S.Sun, J.Shao, J.Yan, S.Yi and X.Tang, Spindle net: Person re-identification with human body region guided feature decomposition and fusion. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition11(6) (2017), 907–915.
36.
P.Arathi, M.S.Vinutha and S.Pai, Survey on Smart City Essentials Using Artificial Intelligence, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE)6(7) 2018.
37.
J.Liu, B.Ni, Y.Yan, P.Zhou, S.Cheng and J.Hu, Pose Transferrable Person Re-Identification [C]. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(4) (2018), 4099–4108.
38.
Z.Zheng, L.Zheng and Y.Yang, Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition12(22) (2017), 3754–3762.
39.
C.Su, J.Li, S.Zhang, J.Xing, W.Gao and Q.Tian, Pose-driven deep convolutional model for person re-identification. in IEEE International Conference on Computer Vision and Pattern Recognition12(22) (2017), 3980–3989.
40.
Y.Lin, L.Zheng, Z.Zheng, Y.Wu and Y.Yang, Improving person re-identification by attribute and identity learning. arXiv preprint arXiv:1703.07220, 2017.
41.
S.Liao, Y.Hu, X.Zhu and S.Z.Li, Person re-identification by local maximal occurrence representation and metric learning. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition10(14) (2015), 2197–2206.
42.
T.Matsukawa, T.Okabe, E.Suzuki and Y.Sato, Hierarchical gaussian descriptor for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1363–1372.
43.
D.Li, X.Chen, Z.Zhang and K.Huang, Learning deep context-aware features over body and latent parts for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(22) (2017), 7398–7407.
44.
R.R.Varior, M.Haloi and G.Wang, Gated siamese convolutional neural network architecture for human reidentification. in Proceedings European Conference on Computer Vision99(12) (2016), 791–808.
45.
T.Xiao, H.Li, W.Ouyang and X.Wang, Learning deep feature representations with domain guided dropout for person re-identification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12(9) (2016), 1249–1258.
46.
Z.Zhong, L.Zheng, D.Cao and S.Li, Re-ranking person reidentification with k-reciprocal encoding. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition11(6) (2017), 3652–3661.
