| Citation: | XIE Pengyu, XU Xin. Multi-scale joint learning for person re-identification[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 613-622. doi: 10.13700/j.bh.1001-5965.2020.0445(in Chinese)
|
The existing person re-identification approaches mainly focus on learning person's local features to match a specific pedestrian across different cameras. However, in the presence of incomplete conditions of pedestrian data such as motion or occlusion of human body parts, background interference, etc., it leads to an increase in the probability of partial loss of pedestrian recognition information. This paper presents a multi-scale joint learning method to extract the fine-grained person feature. This method consists of three subnets, i.e. coarse-grained global feature extraction subnet, fine-grained global feature extraction subnet, and fine-grained local feature extraction subnet. The coarse-grained global feature extraction subnet enhances the diversity of the global feature by fusing semantic information at different levels. The fine-grained global branching unites all local features to learn the correlation among local components of a pedestrian while describing the global features at a fine-grained level. The fine-grained local feature extraction subnet enhances robustness by traversing local features and finding out pedestrian non-significant information. Comparative experiments have been conducted to evaluate the performance of the proposed method against state-of-the-art methods on Market1501, DukeMTMC-ReID, and CUHK03 person re-identification datasets. The experimental results show that the proposed method has the best performance.
| [1] |
LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
|
| [2] |
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 IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 907-915.
|
| [3] |
SUN Y, ZHENG L, YANG Y, et al. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 480-496.
|
| [4] |
ZHENG L, HUANG Y, LU H, et al. Pose-invariant embedding for deep person re-identification[J]. IEEE Transactions on Image Processing, 2019, 28(9): 4500-4509. doi: 10.1109/TIP.2019.2910414
|
| [5] |
WEI L, ZHANG S, YAO H, et al. GLAD: Global-local-alignment descriptor for pedestrian retrieval[C]//Proceedings of the 25th ACM International Conference on Multimedia. New York: ACM Press, 2017: 420-428.
|
| [6] |
ZHENG F, DENG C, SUN X, et al. Pyramidal person re-identification via multi-loss dynamic training[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 8514-8522.
|
| [7] |
FU Y, WEI Y, ZHOU Y, et al. Horizontal pyramid matching for person re-identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 8295-8302.
|
| [8] |
WANG G, YUAN Y, CHEN X, et al. Learning discriminative features with multiple granularities for person re-identification[C]//Proceedings of the 26th ACM International Conference on Multimedia. New York: ACM Press, 2018: 274-282.
|
| [9] |
WANG Z, JIANG J, WU Y, et al. Learning sparse and identity-preserved hidden attributes for person re-identification[J]. IEEE Transactions on Image Processing, 2019, 29(1): 2013-2025.
|
| [10] |
ZENG Z, WANG Z, WANG Z, et al. Illumination-adaptive person re-identification[J]. IEEE Transactions on Multimedia, 2020, 22(12): 3064-3074. doi: 10.1109/TMM.2020.2969782
|
| [11] |
WANG Z, WANG Z, ZHENG Y, et al. Beyond intra-modality: A survey of heterogeneous person re-identification[EB/OL]. (2020-04-27)[2020-07-23]. https://arxiv.org/abs/1905.10048v4.
|
| [12] |
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005: 886-893.
|
| [13] |
LIAO S, HU Y, ZHU X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 2197-2206.
|
| [14] |
KOESTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraints[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2012: 2288-2295.
|
| [15] |
ZHENG L, YANG Y, HAUPTMANN A G. Person re-identification: Past, present and future[EB/OL]. [2020-07-23]. https://arxiv.org/abs/1610.02984.
|
| [16] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 770-778.
|
| [17] |
SU C, LI J, ZHANG S, et al. Pose-driven deep convolutional model for person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3980-3989.
|
| [18] |
SUH Y, WANG J, TANG S, et al. Part-aligned bilinear representations for person re-identification[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 418-437.
|
| [19] |
XU J, ZHAO R, ZHU F, et al. Attention-aware compositional network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 2119-2128.
|
| [20] |
SARFRAZ M S, SCHUMANN A, EBERLE A, et al. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 420-429.
|
| [21] |
ZHENG W S, LI X, XIANG T, et al. Partial person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 4678-4686.
|
| [22] |
LUO H, JIANG W, ZHANG X, et al. AlignedReID++: Dynamically matching local information for person re-identification[J]. Pattern Recognition, 2019, 94: 53-61. doi: 10.1016/j.patcog.2019.05.028
|
| [23] |
SUN Y, XU Q, LI Y, et al. Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 393-402.
|
| [24] |
LI W, ZHU X, GONG S. Harmonious attention network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 2285-2294.
|
| [25] |
LIU X, ZHAO H, TIAN M, et al. HydraPlus-Net: Attentive deep features for pedestrian analysis[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 350-359.
|
| [26] |
SCHROFF F, KALENICHENKO D, PHILBIN J. FaceNet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 815-823.
|
| [27] |
WANG F, XIANG X, CHENG J, et al. Normface: L2 hypersphere embedding for face verification[C]//Proceedings of the 25th ACM International Conference on Multimedia. New York: ACM Press, 2017: 1041-1049.
|
| [28] |
HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification[EB/OL]. (2017-11-17)[2020-07-23]. https://arxiv.org/abs/1703.07737.
|
| [29] |
ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification: A benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1116-1124.
|
| [30] |
LI W, ZHAO R, XIAO T, et al. DeepReID: Deep filter pairing neural network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 152-159.
|
| [31] |
ZHONG Z, ZHENG L, CAO D, et al. Re-ranking person re-identification with k-reciprocal encoding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 1318-1327.
|
| [32] |
ZHENG Z, ZHENG L, YANG Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3754-3762.
|
| [33] |
DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2009: 248-255.
|
| [34] |
PARK H, HAM B. Relation network for person re-identification[EB/OL]. (2017-08-22)[2020-07-23]. https://arxiv.org/abs/1701.07717.
|
| [35] |
ZHENG M, KARANAM S, WU Z, et al. Re-identification with consistent attentive siamese networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 5735-5744.
|
| [36] |
YANG W, HUANG H, ZHANG Z, et al. Towards rich feature discovery with class activation maps augmentation for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2019: 1389-1398.
|
| [37] |
WANG C, ZHANG Q, HUANG C, et al. Mancs: A multi-task attentional network with curriculum sampling for person re-identification[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 365-381.
|
| [38] |
CHEN Y, ZHU X, GONG S. Person re-identification by deep learning multi-scale representations[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 2590-2600.
|
| [39] |
DAI Z, CHEN M, GU X, et al. Batch DropBlock network for person re-identification and beyond[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2019: 3690-3700.
|
| [40] |
SUN Y, ZHENG L, DENG W, et al. SVDNet for pedestrian retrieval[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3820-3828.
|
Figures(5) / Tables(2)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
