基于相机感知的域自适应行人重识别模型

doi:10.16451/j.cnki.issn1003-6059.202405001

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Abstract To solve the problem of low performance in person re-identification caused by large distribution differences between the training and testing sets in corruption scenarios, high background complexity and excessive noise types, a domain adaptive person re-identification model based on camera perception is proposed. The model aligns the image distribution of different cameras during the training phase by introducing and fully utilizing camera information. During the testing phase, temporal information is employed for ranking optimization, reducing the impact of distribution differences between the training and testing sets. The issues of background complexity and noise types are effectively addressed. The model not only effectively mitigates the impact of damaged images from the perspective of dataset processing but also significantly improves the performance of the model in corruption scenarios through quadratic weighting of sorting optimization. Experiments on Market-1501, DukeMTMC-reID and CUHK03 datasets demonstrate the effectiveness of the proposed algorithm.

Key words： Person Re-identification Corruption Scenario Temporal Information Batch Normalization

Received: 06 March 2024

ZTFLH:

TP391.4

Fund:National Natural Science Foundation of China(No.62172229), Natural Science Foundation of Jiangsu Province(No.BK20221349)

Corresponding Authors: HUANG Pu, Ph.D., associate professor. His research interests include pattern recognition and machine lear-ning.

About author:: YANG Zhangjing, Ph.D., associate professor. His research interests include compu-ter vision and pattern recognition. WU Shuli, Master student. His research interests include pattern recognition and machine learning. YANG Guowei, Ph.D., professor. His research interests include machine intelligence theory and methods,intelligent systems and pattern recognition.

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	YANG Zhangjing
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Cite this article:

YANG Zhangjing,WU Shuli,HUANG Pu等. Domain Adaptive Person Re-identification Model Based on Camera Perception[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(5): 383-397.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202405001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I5/383

[1] ZAHRA A, PERWAIZ N, SHAHZAD M, et al. Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends. Pattern Recognition, 2023. DOI: 10.1016/j.patcog.2023.109669.
[2] WANG G C, LAI J H, LIANG W Q, et al. Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 10565-10574.
[3] 王鹏,宋晓宁,吴小俊,等. 用于行人重识别的多类型特征网络.模式识别与人工智能, 2020, 33(10): 879-888.
(WANG P, SONG X N, WU X J, et al. Multi-type Features Network for Person Re-identification. Pattern Recognition and Artificial Intelligence, 2020, 33(10): 879-888.)
[4] HE S T, LUO H, WANG P C, et al. TransReID: Transformer-Based Object Re-identification // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 14993-15002.
[5] TAN F G, LIU W M, HUANG L, et al. Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model. Chinese Journal of Electronics, 2017, 26(5): 905-911.
[6] 张磊,吴晓富,张索非,等. 基于多分支协作的行人重识别网络.模式识别与人工智能, 2021, 34(9): 853-862.
(ZHANG L, WU X F, ZHANG S F, et al. Muti-branch Cooperative Network for Person Re-identification. Pattern Recognition and Artificial Intelligence, 2021, 34(9): 853-862.)
[7] BUADES A, COLL B, MOREL J M. A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling and Simulation, 2005, 4(2): 490-530.
[8] ELAD M, AHARON M. Image Denoising via Sparse and Redundant Representations over Learned Dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.
[9] BUADES A, COLL B, MOREL J M. A Non-Local Algorithm for Image Denoising // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2005. DOI: 10.1109/CVPR.2005.38.
[10] XIE J Y, XU L L, CHEN E H. Image Denoising and Inpainting with Deep Neural Networks // Proc of the 25th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2012, I: 341-349.
[11] GONG Y P, ZENG Z Y. An Effective Data Augmentation for Person Re-identification[C/OL]. [2024-02-17].https://arxiv.org/pdf/2101.08533v1.
[12] FORD N, GILMER J, CARLINI N, et al. Adversarial Examples Are a Natural Consequence of Test Error in Noise[C/OL].[2024-02-17]. https://arxiv.org/pdf/1901.10513.
[13] MIKOŁAJCZYK A, GROCHOWSKI M. Data Augmentation for Improving Deep Learning in Image Classification Problem // Proc of the International Interdisciplinary PhD Workshop. Washington, USA: IEEE, 2018: 117-122.
[14] HENDRYCKS D, MU N, CUBUK E D, et al. AUGMIX: A Simple Data Processing Method to Improve Robustness and Uncertainty[C/OL].[2024-02-17]. https://arxiv.org/abs/1912.02781.
[15] CUBUK E D, ZOPH B, MANÉ D, et al. AutoAugment: Learning Augmentation Strategies from Data // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 113-123.
[16] 彭锦佳,宋鹏鹏,王辉兵. 面向隐私保护的联邦域泛化行人重识别方法.模式识别与人工智能, 2023, 36(7): 634-646.
(PENG J J, SONG P P, WANG H B, et al. Federated Domain Generalization Person Re-identification with Privacy Preserving. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 634-646.)
[17] HENDRYCKS D, BASART S, MU N, et al. The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 8320-8329.
[18] CALIAN D A, STIMBERG F, WILES O, et al. Defending Against Image Corruptions through Adversarial Augmentations[C/OL].[2024-02-17]. https://arxiv.org/pdf/2104.01086.
[19] ZHANG R. Making Convolutional Networks Shift-Invariant Again. Journal of Machine Learning Research, 2019, 97: 7324-7334.
[20] XIE Q Z, LUONG M T, HOVY E, et al. Self-Training with Noisy Student Improves ImageNet Classification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Wa-shington, USA: IEEE, 2020: 10684-10695.
[21] WANG H T, XIAO C W, KOSSAIFI J, et al. AugMax: Adversa-rial Composition of Random Augmentations for Robust Training[C/OL].[2024-02-17]. https://openreview.net/pdf?id=P5MtdcVdFZ4.
[22] GEIRHOS R, TEMME C R M, RAUBER J, et al. Generalisation in Humans and Deep Neural Networks // Proc of the 32nd International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2018: 7549-7561.
[23] DATTA R, JOSHI D, LI J, et al. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys, 2008, 40(2). DOI: 10.1145/1348246.1348248.
[24] LUO H, JIANG W, GU Y Z, et al. A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification. IEEE Tran- sactions on Multimedia, 2019, 22(10): 2597-2609.
[25] HERMANS A, BEYER L, LEIBE B. In Defense of the Triplet Loss for Person Re-Identification[C/OL]. [2024-02-17].https://arxiv.org/pdf/1703.07737.
[26] IOFFE S, SZEGEDY C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift // Proc of the 32nd International Conference on Machine Learning. San Diego, USA: JMLR, 2015: 448-456.
[27] ZHONG Z, ZHENG L, CAO D L, et al. Re-ranking Person Re-identification with k-Reciprocal Encoding // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 3652-3661.
[28] LIAO S C, SHAO L. Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lif-ting // Proc of the 16th European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 456-474.
[29] ZHANG Z L, SABUNCU M. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels // Proc of the 32nd International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2018: 8792-8802.
[30] CHEN M H, WANG Z Q, ZHENG F. Benchmarks for Corruption Invariant Person Re-identification[C/OL]. [2024-02-17].https://arxiv.org/pdf/2111.00880.
[31] ZHENG L, SHEN L Y, TIAN L, et al. Scalable Person Re-identification: A Benchmark // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1116-1124.
[32] LI W, ZHAO R, XIAO T, et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 152-159.
[33] RISTANI E, SOLERA F, ZOU R, et al. Performance Measures and a Data Set for Multi-target, Multi-camera Tracking // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 17-35.
[34] WEI L H, ZHANG S L, GAO W, et al. Person Transfer GAN to Bridge Domain Gap for Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 79-88.
[35] HU J, SHEN L, SUN G. Squeeze-and-Excitation Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2018: 7132-7141.
[36] PAN X G, LUO P, SHI J P, et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 484-500.
[37] LUO H, JIANG W, ZHANG X, et al. AlignedReID++: Dynamically Matching Local Information for Person Re-identification. Pa-ttern Recognition, 2019, 94: 53-61.
[38] HERZOG F, JI X B, TEEPE T, et al. Lightweight Multi-branch Network for Person Re-identification // Proc of the IEEE International Conference on Image Processing. Washington, USA: IEEE, 2021: 1129-1133.
[39] QUISPE R, PEDRINI H. Top-DB-Net: Top Dropblock for Activation Enhancement in Person Re-identification // Proc of the 25th International Conference on Pattern Recognition. Washington, USA: IEEE, 2021: 2980-2987.
[40] XIE B, WU X F, ZHANG S F, et al. Learning Diverse Features with Part-Level Resolution for Person Re-identification // Proc of the 3rd Chinese Conference on Pattern Recognition and Computer Vision. Berlin, Germany: Springer, 2020: 16-28.
[41] GONG Y P, ZENG Z Y, CHEN L W, et al. A Person Re-identification Data Augmentation Method with Adversarial Defense Effect[C/OL].[2024-02-17]. https://arxiv.org/pdf/2101.08783.
[42] YE M, SHEN J B, LIN G J, et al. Deep Learning for Person Re-identification: A Survey and Outlook. IEEE Transactions on Pa-ttern Analysis and Machine Intelligence, 2022, 44(6): 2872-2893.
[43] ZHOU K Y, YANG Y X, CAVALLARO A, et al. Omni-Scale Feature Learning for Person Re-identification // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 3701-3711.