دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات13
تعداد شماره‌ها206
تعداد مقالات2,049
تعداد مشاهده مقاله2,721,266
تعداد دریافت فایل اصل مقاله1,972,911
Iranpoor, R., Zahiri, S. H.. (1403). Image Recreating in improving the Performance of Architectures for Person Re-identification. فناوری آموزش, 12(2), 401-408. doi: 10.22061/jecei.2024.10446.706
R. Iranpoor; S. H. Zahiri. "Image Recreating in improving the Performance of Architectures for Person Re-identification". فناوری آموزش, 12, 2, 1403, 401-408. doi: 10.22061/jecei.2024.10446.706
Iranpoor, R., Zahiri, S. H.. (1403). 'Image Recreating in improving the Performance of Architectures for Person Re-identification', فناوری آموزش, 12(2), pp. 401-408. doi: 10.22061/jecei.2024.10446.706
Iranpoor, R., Zahiri, S. H.. Image Recreating in improving the Performance of Architectures for Person Re-identification. فناوری آموزش, 1403; 12(2): 401-408. doi: 10.22061/jecei.2024.10446.706

Image Recreating in improving the Performance of Architectures for Person Re-identification

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 9، دوره 12، شماره 2، مهر 2024، صفحه 401-408    اصل مقاله (955.97 K)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10446.706
نویسندگان
R. Iranpoor؛ S. H. Zahiri*
Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 22 دی 1402،  تاریخ بازنگری: 11 فروردین 1403،  تاریخ پذیرش: 15 فروردین 1403 
چکیده
Background and Objectives: Re-identifying individuals due to its capability to match a person across non-overlapping cameras is a significant application in computer vision. However, it presents a challenging task because of the large number of pedestrians with various poses and appearances appearing at different camera viewpoints. Consequently, various learning approaches have been employed to overcome these challenges. The use of methods that can strike an appropriate balance between speed and accuracy is also a key consideration in this research.
Methods: Since one of the key challenges is reducing computational costs, the initial focus is on evaluating various methods. Subsequently, improvements to these methods have been made by adding components to networks that have low computational costs. The most significant of these modifications is the addition of an Image Re-Retrieval Layer (IRL) to the Backbone network to investigate changes in accuracy.
Results: Given that increasing computational speed is a fundamental goal of this work, the use of MobileNetV2 architecture as the Backbone network has been considered. The IRL block has been designed for minimal impact on computational speed. By examining this component, specifically for the CUHK03 dataset, there was a 5% increase in mAP and a 3% increase in @Rank1. For the Market-1501 dataset, the improvement is partially evident. Comparisons with more complex architectures have shown a significant increase in computational speed in these methods.
Conclusion: Reducing computational costs while increasing relative recognition accuracy are interdependent objectives. Depending on the specific context and priorities, one might emphasize one over the other when selecting an appropriate method. The changes applied in this research can lead to more optimal results in method selection, striking a balance between computational efficiency and recognition accuracy.
کلیدواژه‌ها
Person Re-identification؛ Deep Learning؛ Convolutional Neural Network؛ Image Detection
مراجع

[1] W. Wei, W. Yang, E. Zuo, Y. Qian, L. Wang, "Person re-identification based on deep learning—An overview," J. Visual Commun. Image Represent., 82: 103418, 2022.

[2] M. Farenzena, L. Bazzani, A. Perina, V. Murino, M. Cristani, "Person re-identification by symmetry-driven accumulation of local features," in Proc. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 2360-2367, 2020.

[3] W. S. Zheng, S. Gong, T. Xiang, "Person re-identification by probabilistic relative distance comparison," in Proc. CVPR 2011: 649-656, 2011.

[4] D. Wu et al., "Deep learning-based methods for person re-identification: A comprehensive review," Neurocomput., 337: 354-371, 2019.

[5] Y. Sun, L. Zheng, Y. Yang, Q. Tian, S. Wang, "Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)," in Proc. the European Conference on Computer Vision (ECCV): 480-496, 2018.

[6] Z. Zhong, L. Zheng, Z. Zheng, S. Li, Y. Yang, "Camera style adaptation for person re-identification," in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 5157-5166, 2018.

[7] H. J. Mohammed et al., "ReID-DeePNet: A hybrid deep learning system for person re-identification," Math., 10(19): 3530, 2022.

[8] Y. Zhu et al., "Multiscale global-aware channel attention for person re-identification," J. Visual Commun. Image Represent., 90: 103714, 2023.

[9] L. Zhao, X. Li, Y. Zhuang, J. Wang, "Deeply-learned part-aligned representations for person re-identification," in Proc. the IEEE International Conference on Computer Vision: 3219-3228, 2017.

[10] K. Zhu et al., "Aaformer: Auto-aligned transformer for person re-identification," IEEE Trans. Neural Networks Learn. Syst., 2023.

[11] Y. Cho, W. J. Kim, S. Hong, S. E. Yoon, "Part-based pseudo label refinement for unsupervised person re-identification," in Proc. the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 7308-7318, 2022.

[12] Y. Chen, H. Wang, X. Sun, B. Fan, C. Tang, H. Zeng, "Deep attention aware feature learning for person re-identification," Pattern Recognit., 126: 108567, 2022.

[13] D. Gray, H. Tao, "Viewpoint invariant pedestrian recognition with an ensemble of localized features," in Proc. ECCV 2008: 262-275, 2008.

[14] C. C. Loy, T. Xiang, S. Gong, "Multi-camera activity correlation analysis," in Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition: 1988-1995, 2009.

[15] W. Li, R. Zhao, X. Wang, "Human reidentification with transferred metric learning," in Proc. 11th Asian Conference on Computer Vision, Part I 11: 31-44, 2013.

[16] W. Li, R. Zhao, T. Xiao, X. Wang, "Deepreid: Deep filter pairing neural network for person re-identification," in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 152-159, 2014.

[17] L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, Q. Tian, "Scalable person re-identification: A benchmark," in Proc. the IEEE International Conference on Computer Vision: 1116-1124, 2015.

[18] E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi, "Performance measures and a data set for multi-target, multi-camera tracking," in Proc. European Conference on Computer Vision: 17-35, 2016.

[19] X. Wang, G. Doretto, T. Sebastian, J. Rittscher, P. Tu, “Shape and appearance context modeling,” in Proc. ICCV 2007: 1–8, 2007.

[20] M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, S. C. Hoi, "Deep learning for person re-identification: A survey and outlook," IEEE Trans. Pattern Anal. Mach. Intell., 44(6): 2872-2893, 2021.

[21] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, "You only look once: Unified, real-time object detection," in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 779-788, 2006.

[22] Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, J. Sun, "Detnet: A backbone network for object detection," arXiv preprint arXiv:1804.06215, 2018.

[23] S. Targ, D. Almeida, K. Lyman, "Resnet in resnet: Generalizing residual architectures," arXiv preprint arXiv:1603.08029, 2016.

[24] S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, "Aggregated residual transformations for deep neural networks," in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 1492-1500, 2017.

[25] A. G. Howard et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.

[26] J. Zang, L. Wang, Z. Liu, Q. Zhang, G. Hua, N. Zheng, "Attention-based temporal weighted convolutional neural network for action recognition," in Proc. 14th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Innovations (AIAI 2018):  97-108, 2018.

[27] F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer, "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size," arXiv preprint arXiv:1602.07360, 2016.

[28] C. Fran, "Deep learning with depth wise separable convolutions," in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[29] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks," in Proc. the IEEE Conference on Computer Vision and Pattern Recognition: 4510-4520, 2018.

[30] Y. Sun, L. Zheng, Y. Li, Y. Yang, Q. Tian, S. Wang, "Learning part-based convolutional features for person re-identification," IEEE Trans. Pattern Anal. Mach. Intell., 43(3): 902-917, 2019.

[31] Y. Zhu, S. Newsam, "Densenet for dense flow," in Proc. 2017 IEEE International Conference on Image Processing (ICIP): 790-794, 2017.

 

آمار
تعداد مشاهده مقاله: 95
تعداد دریافت فایل اصل مقاله: 51


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب