| Citation: | LI Y L，CHENG D Q，LI J H，et al. Cross-domain person re-identification based on progressive attention and block occlusion[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（11）：3167-3176 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0025
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A cross-domain person re-identification method based on progressive attention and block occlusion is proposed in order to address the issue of missing feature matching caused by occlusion and neglect of fine-grained discriminative features in cross-domain person re-identification. This method realizes feature matching under spatial misalignment by learning multi-granularity discriminative features of regions where people are not blocked. The progressive attention module gradually divides the features into multiple local blocks, learns the discriminative features of each block in turn, and perceives the foreground information from coarse to fine, which solves the problem that the current network cannot extract multi-level distinguishing features and improves the feature matching ability of the model. In addition, the progressive block occlusion module is well adapted to the gradually stronger learning ability of the model. The robustness of the model proposed in this paper is finally effectively improved in the case of occlusion by effectively generating occlusion data from easy to difficult, effectively extracting the identifying features of non-occlusion areas, and then solving the problem of the model misidentifying occluded samples. The experimental results show that the algorithm has significant advantages compared with the current mainstream algorithms in the two indicators of first hit rate and mean average accuracy. Especially when compared with the QAConv person re-identification algorithm published in CVPR in 2020, the two indicators of this algorithm on the DukeMTMC-reID dataset (MSMT17→DukeMTMC-reID) are 2.3% and 6.2% higher, respectively, and the algorithm in this paper can realize cross-domain person re-identification more effectively. Additionally, the DukeMTMC-reID→Occluded-Duke dataset shows good recognition results for the system in this article, with the two indicators reaching 49.5% and 39.0%, respectively.
| [1] |
WANG G S, YUAN Y F, 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, 2018: 274-282.
|
| [2] |
ZHAO C R, LV X B, DOU S G, et al. Incremental generative occlusion adversarial suppression network for person ReID[J]. Transactions on Image Processing, 2021, 30: 4212-4224. doi: 10.1109/TIP.2021.3070182
|
| [3] |
SUN Y F, 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: 501-518.
|
| [4] |
LAVI B, ULLAH I, FATAN M, et al. Survey on reliable deep learning-based person re-identification models: Are we there yet?[EB/OL]. (2020-04-30)[2022-01-01]. https://arxiv.org/abs/2005.0035501.
|
| [5] |
FU Y, WEI Y C, WANG G S, et al. Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 6111-6120.
|
| [6] |
WU J L, LIAO S C, LEI Z, et al. Clustering and dynamic sampling based unsupervised domain adaptation for person re-identification[C]//Proceedings of the IEEE International Conference on Multimedia and Expo. Piscataway: IEEE Press, 2019: 886-891.
|
| [7] |
LI J H, CHENG D Q, LIU R H, et al. Unsupervised person re-identification based on measurement axis[J]. IEEE Signal Processing Letters, 2021, 28: 379-383. doi: 10.1109/LSP.2021.3055116
|
| [8] |
MEKHAZNI D, BHUIYAN A, EKLADIOUS G, et al. Unsupervised domain adaptation in the dissimilarity space for person re-identification[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2020: 159-174.
|
| [9] |
WANG D K, ZHANG S L. Unsupervised person re-identification via multi-label classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 10978-10987.
|
| [10] |
CHEN G Y, LU Y H, LU J W, et al. Deep credible metric learning for unsupervised domain adaptation person re-identification[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2020: 643-659.
|
| [11] |
张晓伟, 吕明强, 李慧. 基于局部语义特征不变性的跨域行人重识别[J]. 北京航空航天大学学报, 2020, 46(9): 1682-1690.
ZHANG X W, LYU M Q, LI H. Cross-domain person re-identification based on partial semantic feature invariance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1682-1690(in Chinese).
|
| [12] |
ZHONG Z, ZHENG L, ZHENG Z D, et al. CamStyle: A novel data augmentation method for person re-identification[J]. IEEE Transactions on Image Processing, 2019, 28(3): 1176-1190. doi: 10.1109/TIP.2018.2874313
|
| [13] |
ZENG K W, NING M N, WANG Y H, et al. Hierarchical clustering with hard-batch triplet loss for person re-identification [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 13654-13662.
|
| [14] |
LIN Y T, XIE L X, WU Y, et al. Unsupervised person re-identification via softened similarity learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 3387-3396.
|
| [15] |
LIN Y T, DONG X Y, ZHENG L A, et al. A bottom-up clustering approach to unsupervised person re-identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2019, 33(1): 8738-8745.
|
| [16] |
YE M, SHEN J B, LIN G J, et al. Deep learning for person re-identification: A survey and outlook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6): 2872-2893. doi: 10.1109/TPAMI.2021.3054775
|
| [17] |
ZHANG X Y, CAO J W, SHEN C H, et al. Self-training with progressive augmentation for unsupervised cross-domain person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 8221-8230.
|
| [18] |
ZHONG Z, ZHENG L, CAO D L, 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: 3652-3661.
|
| [19] |
ZHUANG Z J, WEI L H, XIE L X, et al. Rethinking the distribution gap of person re-identification with camera-based batch normalization[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2020: 140-157.
|
| [20] |
LIAO S C, SHAO L. Interpretable and generalizable person re-identification with query-adaptive convolution and temporal lifting[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2020: 456-474.
|
| [21] |
廖华年, 徐新. 基于注意力机制的跨分辨率行人重识别[J]. 北京航空航天大学学报, 2021, 47(3): 605-612.
LIAO H N, XU X. Cross-resolution person re-identification based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 605-612(in Chinese).
|
| [22] |
ZHANG Z Z, LAN C L, ZENG W J, et al. Relation-aware global attention for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 3183-3192.
|
| [23] |
WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 3-19.
|
| [24] |
HE K M, ZHANG X Y, REN S Q, 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.
|
| [25] |
LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327. doi: 10.1109/TPAMI.2018.2858826
|
| [26] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 7132-7141.
|
| [27] |
ZHENG L, SHEN L Y, TIAN L, et al. Scalable person re-identification: A benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2016: 1116-1124.
|
| [28] |
RISTANI E, SOLERA F, ZOU R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2016: 17-35.
|
| [29] |
WEI L H, ZHANG S L, GAO W, et al. Person transfer GAN to bridge domain gap for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 79-88.
|
| [30] |
MIAO J X, WU Y, LIU P, et al. Pose-guided feature alignment for occluded person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 542-551.
|
| [31] |
ZHAO R, OUYANG W, WANG X. Person re-identification by salience matching [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2013: 2528-2535
|
| [32] |
ZHU X Z, CHENG D Z, ZHANG Z, et al. An empirical study of spatial attention mechanisms in deep networks[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 6687-6696.
|
| [33] |
ZHONG Z, ZHENG L, LUO Z M, et al. Invariance matters: Exemplar memory for domain adaptive person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 598-607.
|
| [34] |
YANG Q Z, YU H X, WU A C, et al. Patch-based discriminative feature learning for unsupervised person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 3628-3637.
|
| [35] |
YU H X, ZHENG W S, WU A C, et al. Unsupervised person re-identification by soft multilabel learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 2143-2152.
|
| [36] |
WU A C, ZHENG W S, LAI J H. Unsupervised person re-identification by camera-aware similarity consistency learning[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 6921-6930.
|
| [37] |
QI L, WANG L, HUO J, et al. A novel unsupervised camera-aware domain adaptation framework for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 8079-8088.
|
| [38] |
ZHANG W Y, ZHU L, LU L. Improving the style adaptation for unsupervised cross-domain person re-identification[C]//Proceedings of the International Joint Conference on Neural Networks. Piscataway: IEEE Press, 2020: 1-8.
|
| [39] |
QIAN X L, FU Y W, XIANG T, et al. Pose-normalized image generation for person re-identification[C]//Proceedings of the European Conference on Computer Vision. Berlin: Springer, 2018: 661-678.
|
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