مجله ماشین بینایی و پردازش تصویر

مجله ماشین بینایی و پردازش تصویر

داده افزایی با استفاده از شبکه‌های مولد تخاصمی جهت بهبود باز تشخیص افراد

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکترای رشته برق - مخابرات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 عضو هیات علمی پژوهشگاه ارتباطات و فناوری اطلاعات
3 دانشکده مهندسی برق و کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
در سال‌های اخیر، پژوهش‌ها در زمینه بازتشخیص افراد به‌طور پیوسته پیشرفت کرده‌اند. در این راستا، شبکه‌های عصبی متخاصم به موفقیت چشمگیری دست یافته‌اند و به عنوان یک رویکرد برجسته در این حوزه شناخته می‌شوند. در این مقاله، با بررسی کاربرد انواع شبکه‌های متخاصم در بازتشخیص افراد، از ترکیب شبکه ATNet و مدل Pix2Pix برای داده‌افزایی استفاده شده است. مدل Pix2Pix که در تبدیل تصویر به تصویر در زمینه‌های مختلف موفقیت‌آمیز بوده، از الگوریتم PatchGAN به عنوان متمایزکننده و U-Net به عنوان تولیدکننده بهره می‌گیرد. روش پیشنهادی برای داده‌افزایی مبتنی بر وجود تصاویر فرد مورد نظر از چهار جهت (روبه‌رو، پشت، سمت چپ و راست) است. پس از دسته‌بندی تصاویر در مجموعه‌داده‌های Market-1501 و CUHK03، تصاویر نماهای ناموجود با استفاده از شبکه ATNet تولید شده‌اند. مقایسه های انجام‌شده نشان‌دهنده بهبود عملکرد روش‌های پیشرو در بازتشخیص افراد با داده افزایی پیشنهادی در این مقاله است.
کلیدواژه‌ها

عنوان مقاله English

Enhancing Person Re-Identification using GAN-Based Data Augmentation

نویسندگان English

Hadi Kaveh 1
Mohammad-Shahram Moin 2
Farbod Razzazi 3
1 Ph.D. student of Department of Electrical Engineering, SR.C., Islamic Azad University, Tehran, Iran
2 IT Faculty, ICT Research Institute
3 Faculty of Electrical Engineering, SR.C., Islamic Azad University, Tehran, Iran
چکیده English

In recent years, research in the field of person re-identification has made consistent progress. In this context, adversarial neural networks have achieved remarkable success and are recognized as a prominent approach in this domain. This paper explores the application of various adversarial networks in person re-identification, utilizing a combination of the ATNet network and the Pix2Pix model for data augmentation. The Pix2Pix model, which has been successful in image-to-image translation across various fields, employs the PatchGAN algorithm as the discriminator and U-Net as the generator. The proposed data augmentation method is based on the availability of images of the target individual from four perspectives (front, back, left, and right). After categorizing the images in the Market-1501 and CUHK03 datasets, images of missing viewpoints were generated using the ATNet network. The results of simulations demonstrate that, using the proposed data augmentation method, the performance of person re-identification improves compared to the state-of-the-art methods.

کلیدواژه‌ها English

Person Re-Identification
Data augmentation
GAN
Pix2Pix GAN
ATNet Network
U-Net Model
[1]
Bhuiyan, Md Roman, et al. "Video analytics using deep learning for crowd analysis: a review." Multimedia Tools and Applications 81.19 (2022): 27895-27922.
[2]
Zheng, Zhedong, Liang Zheng, and Yi Yang. "A discriminatively learned cnn embedding for person reidentification." ACM transactions on multimedia computing, communications, and applications (TOMM) 14.1 (2017): 1-20.
[3]
Alqahtani, Hamed, Manolya Kavakli-Thorne, and Gulshan Kumar. "Applications of generative adversarial networks (gans): An updated review." Archives of Computational Methods in Engineering 28 (2021): 525-552
[4]
Creswell, Antonia, et al. "Generative adversarial networks: An overview." IEEE signal processing magazine 35.1 (2018): 53-65.
[5]
Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[6]
Liu, Ming-Yu, and Oncel Tuzel. "Coupled generative adversarial networks." Advances in neural information processing systems 29 (2016).
[7]
Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.
[8]
Cao, Chengzhi, et al. "Event-guided person re-identification via sparse-dense complementary learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
[9]
Asperti, Andrea, Salvatore Fiorilla, and Lorenzo Orsini. "A generative approach to person reidentification." Sensors 24.4 (2024): 1240.
[10]
Chen, Weihua, et al. "Beyond appearance: a semantic controllable self-supervised learning framework for human-centric visual tasks." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.
[11]
Zhong, Zhun, et al. "Camera style adaptation for person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[12]
Wei, Longhui, et al. "Person transfer gan to bridge domain gap for person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[13]
Liu, Jiawei, et al. "Adaptive transfer network for cross-domain person re-identification." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
[14]
Liu, Chong, Xiaojun Chang, and Yi-Dong Shen. "Unity style transfer for person re-identification." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
[15]
Pan, Xingang, et al. "Two at once: Enhancing learning and generalization capacities via ibn-net." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
[16]
Zhang, Taiping, et al. "Face recognition under varying illumination using gradientfaces." IEEE Transactions on image processing 18.11 (2009): 2599-2606.
[17]
Ledig, Christian, et al. "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[18]
Zheng, Liang, et al. "Scalable person re-identification: A benchmark." Proceedings of the IEEE international conference on computer vision. 2015.
[19]
Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[20]
Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.
[21]
Ming, Zhangqiang, et al. "Deep learning-based person re-identification methods: A survey and outlook of recent works." Image and Vision Computing 119 (2022): 104394
[22]
Zheng, Zhedong, Liang Zheng, and Yi Yang. "Unlabeled samples generated by gan improve the person re-identification baseline in vitro." Proceedings of the IEEE international conference on computer vision. 2017.
[23]
Qian, Xuelin, et al. "Pose-normalized image generation for person re-identification." Proceedings of the European conference on computer vision (ECCV). 2018.
[24]
Liu, Jinxian, et al. "Pose transferrable person re-identification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[25]
Huang, Yan, et al. "Multi-pseudo regularized label for generated data in person re-identification." IEEE Transactions on Image Processing 28.3 (2018): 1391-1403.
[26]
Zheng, Zhedong, et al. "Joint discriminative and generative learning for person re-identification." proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
[27]
Ge, Yixiao, et al. "Fd-gan: Pose-guided feature distilling gan for robust person re-identification." Advances in neural information processing systems 31 (2018).
 
دوره 12، شماره 1
تابستان 1404
صفحه 47-59

  • تاریخ دریافت 01 مرداد 1403
  • تاریخ بازنگری 04 اردیبهشت 1404
  • تاریخ پذیرش 06 اردیبهشت 1404