| Citation: | JI G K，WANG R，PENG S F. Person re-identification method based on attention mechanism and CondConv[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：655-662 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0454
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Person Re-identification is an important part of the field of computer vision, but it is easily affected by the actual collection environment of person images, resulting in insufficient expression of person features and further leading to low model accuracy. An improved person re-identification method based on attention mechanism and CondConv is proposed to fully express pedestrian features. The attention mechanism is introduced into the feature extraction network ResNet50, and the key information in the input image space and channel is weighted, while suppressing possible noise. The CondConv is introduced into the backbone network and the convolution kernel parameters are dynamically adjusted to improve the capacity and performance of the model while maintaining efficient reasoning. Mainstream data sets such as Market1501, MSMT17 and DukeMTMC-ReID are used to evaluate the improved method. Rank-1 is increased by 1.1%, 2.4% and 1.3% respectively, and mAP is increased by 0.5%, 2.3% and 1.3%; respectively. The results show that the improved method can better express person features and improve recognition accuracy.
| [1] |
MING Z Q, ZHU M K, WANG X K, et al. Deep learning-based person re-identification methods: A survey and outlook of recent works[J]. Image and Vision Computing, 2022, 119: 104394.
|
| [2] |
孙义博, 张文靖, 王蓉, 等. 基于通道注意力机制的行人重识别方法[J]. 北京航空航天大学学报, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684
SUN Y B, ZHANG W J, WANG R, et al. Pedestrian re-identification method based on channel attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 881-889 (in Chinese). doi: 10.13700/j.bh.1001-5965.2020.0684
|
| [3] |
WIECZOREK M, RYCHALSKA B, DąBROWSKI J. On the unreasonable effectiveness of centroids in image retrieval[C]//Neural Information Processing: 28th International Conference. Berlin: Springer, 2021: 212-223.
|
| [4] |
LI H J, WU G J, ZHENG W S. Combined depth space based architecture search for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2021: 6729-6738.
|
| [5] |
YAN C, PANG G S, BAI X, et al. Beyond triplet loss: Person re-identification with fine-grained difference-aware pairwise loss[J]. IEEE Transactions on Multimedia, 2021, 24: 1665-1677.
|
| [6] |
ZHOU K Y, YANG Y X, CAVALLARO A, et al. Learning generalisable omni-scale representations for person re-identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 5056-5069.
|
| [7] |
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).
|
| [8] |
LIU J W, ZHA Z J, WU W, et al. Spatial-temporal correlation and topology learning for person re-identification in videos[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2021: 4370-4379.
|
| [9] |
WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision. Munich: ECCV, 2018: 3-19.
|
| [10] |
YANG B, BENDER G, LE Q V, et al. Condconv: Conditionally parameterized convolutions for efficient inference[EB/OL]. (2019-01-10) [2022-02-19]. https://arxiv.org/abs/1904.04071.html.
|
| [11] |
WIECZOREK M, MICHALOWSKI A, WROBLEWSKA A, et al. A strong baseline for fashion retrieval with person re-identification models[C]//International Conference on Neural Information Processing. Berlin: Springer, 2020: 294-301.
|
| [12] |
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.
|
| [13] |
ZHENG L, SHEN L, TIAN L Y, et al. Scalable person re-identification: A benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1116-1124.
|
| [14] |
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 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018 : 79-88.
|
| [15] |
ZHENG Z D, 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.
|
| [16] |
LI Y L, HE J F, ZHANG T Z, et al. Diverse part discovery: Occluded person re-identification with part-aware trans-former[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2021: 2898–2907.
|
| [17] |
ZHANG Z, ZHANG H J, LIU S. Person re-identification using hetero-geneous local graph attention networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2021: 12136–12145.
|
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