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Cogent Engineering Volume 10, 2023 - Issue 1
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COMPUTER SCIENCE

Combined visual and spatial-temporal information for appearance change person re-identification

Shavantrevva Bilakeri1 Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, IndiaView further author information
&
Karunakar A Kotegar1 Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, IndiaCorrespondence[email protected]
View further author information
Article: 2197695 | Received 27 Jan 2023, Accepted 25 Mar 2023, Published online: 25 Apr 2023

References

  • Bhujel, N., Jun, L., Yun, Y. W., & Wang, H. (2020). Towards understanding and inferring the crowd: Guided second order attention networks and re-identification for multi-object tracking. Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), The Caesars Forum Conventional Center in Las Vegas, USA (pp. 9999–17). IEEE.
  • Bilakeri, S., & K, A. K. (2022). Multi-object tracking by multi-feature fusion to associate all detected boxes. Cogent Engineering, 9(1), 2151553. https://doi.org/10.1080/23311916.2022.2151553
  • Bilakeri, S., & Karunakar, A. K. (2023). Triplet Multi-task Learning Strategy for Person Re-identification Using Deep Learning. Proceedings of International Conference on Data Science and Applications: ICDSA 2022. School of Mobile Computing and Communication, Jadavpur University, Kolkata, India, 2, 447–461. Springer Nature.
  • Bilakeri, S., & Kotegar, K. (2022) Strong baseline with auto-encoder for scale-invariant person re-identification. Procedings of the 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), JNN College of Engineering, Shimoga, Karnataka, India (pp 1–6). IEEE.
  • Chen, J., Jiang, X., Wang, F., Zhang, J., Zheng, F., Sun, X., & Zheng, W. S. (2021). Learning 3d shape feature for texture-insensitive person re-identification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, United States (pp. 8146–8155).
  • Chen, F., Wang, N., Tang, J., & Zhu, F. (2021). A feature disentangling approach for person re-identification via self-supervised data augmentation. Applied Soft Computing, 100, 106939. https://doi.org/10.1016/j.asoc.2020.106939
  • Che, J., Zhang, Y., Yang, Q., & He, Y. (2022). Research on person re-identification based on posture guidance and feature alignment. Multimedia Systems, 29(2), 1–8. https://doi.org/10.1007/s00530-022-01016-3
  • Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., & Jiao, J. (2018). Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA. (pp. 994–1003).
  • Fan, H., Zhu, L., Yang, Y., & Wu, F. (2020). Recurrent attention network with reinforced generator for visual dialog. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(3), 1–16. https://doi.org/10.1145/3390891
  • Gao, Z., Wei, H., Guan, W., Nie, W., Liu, M., & Wang, M. (2021). Multigranular visual-semantic embedding for cloth-changing person re-identification. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon Congress Center, Portugal (pp. 3703–3711).
  • Gao, Z., Wei, H., Guan, W., Nie, W., Liu, M., Wang, M. (2022). Multigranular visual-semantic embedding for cloth-changing person re-identification. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon Congress Center, Portugal (pp. 3703–3711).
  • Gao, Z., Wei, H., Guan, W., Nie, J., Wang, M., & Chen, S. (2022). A semantic-aware attention and visual shielding network for cloth-changing person re-identification.
  • Gu, X., Chang, H., Ma, B., Bai, S., Shan, S., Chen, X. (2022) Clothes-changing person re-identification with rgb modality only. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans Ernest N. Morial Convention Center New Orleans, Louisiana (pp. 1060–1069).
  • Hong, P., Wu, T., Wu, A., Han, X., Zheng, W. S. (2021) Fine-grained shape-appearance mutual learning for cloth-changing person re-identification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA (pp. 10513–10522).
  • Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii (pp. 4700–4708).
  • Huang, H., Yang, W., Chen, X., Zhao, X., Huang, K., Lin, J., Huang, G., & Du, D. (2018). Eanet: Enhancing alignment for cross-domain person re-identification.
  • Jia, Z., Li, Y., Tan, Z., Wang, W., Wang, Z., & Yin, G. (2022). Domain-invariant feature extraction and fusion for cross-domain person re-identification. The Visual Computer, 39(3), 1–12. https://doi.org/10.1007/s00371-022-02398-1
  • Jia, X., Zhong, X., Ye, M., Liu, W., & Huang, W. (2022). Complementary data augmentation for cloth-changing person re-identification. IEEE Transactions on Image Processing, 31, 4227–4239. https://doi.org/10.1109/TIP.2022.3183469
  • Jin, X., He, T., Zheng, K., Yin, Z., Shen, X., Huang, Z., Feng, R., Huang, J., Hua, X. S., & Chen, Z. (2021). Cloth-changing person re-identification from a single image with gait prediction and regularization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14278–14287).
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(6), 1097–1105.
  • Li, S., Bak, S., Carr, P., &, and Wang, X. (2018). Diversity regularized spatiotemporal attention for video-based person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA (pp. 369–378).
  • Lv, J., Chen, W., Li, Q., Yang, C. (2018) Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7948–7956
  • Movassagh, A. A., Alzubi, J. A., Gheisari, M., Rahimi, M., Mohan, S., Abbasi, A. A., & Nabipour, N. (2021). Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. Journal of Ambient Intelligence and Humanized Computing, 1–9. https://doi.org/10.1007/s12652-020-02623-6
  • Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065–1076. https://doi.org/10.1214/aoms/1177704472
  • Pervaiz, N., Fraz, M., & Shahzad, M. (2022). Per-former: Rethinking person re-identification using transformer augmented with self-attention and contextual mapping. The Visual Computer, 1–16. https://doi.org/10.1007/s00371-022-02577-0
  • Qian, X., Fu, Y., Xiang, T., Wang, W., Qiu, J., Wu, Y., Jiang, Y. G., & Xue, X. (2018). Pose-normalized image generation for person re-identification. Proceedings of the European conference on computer vision (ECCV), Munich, Germany (pp. 650–667).
  • Qian, X., Wang, W., Zhang, L., Zhu, F., Fu, Y., Xiang, T., Jiang, Y. G., & Xue, X. (2020). Long-term cloth-changing person re-identification. Proceedings of the Asian Conference on Computer Vision, Kyoto.
  • Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019). An attention enhanced graph convolutional lstm network for skeleton-based action recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, California (pp. 1227–1236).
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.
  • Suh, Y., Wang, J., Tang, S., Mei, T., & Lee, K. M. (2018). Part-aligned bilinear representations for person re-identification. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, (pp. 402–419).
  • Sun, W., Xie, J., Qiu, J., & Ma, Z. (2021). Part uncertainty estimation convolutional neural network for person re-identification. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage-Alaska, Denaʼina Civic and Convention Center, (pp. 2304–2308).
  • Sun, Y., Zheng, L., Yang, Y., Tian, Q., & Wang, S. (2018). Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). Proceedings of the European conference on computer vision (ECCV), Tel Aviv, Israel (pp. 480–496).
  • Wang, X., Huang, Y., Wang, Q., Chen, Y., & Shen, Y. (2020). Multi-stream refining network for person re-identification. IEEE Access, 9, 6596–6607. https://doi.org/10.1109/ACCESS.2020.3048119
  • Wang, G., Lai, J., Huang, P., & Xie, X. (2019). Spatial-temporal person re-identification. Proceedings of the AAAI conference on artificial intelligence, Honolulu, Hawaii, 33, 8933–8940.
  • Wang, G., Lai, J., & Xie, X. (2017). P2snet: Can an image match a video for person re-identification in an end-to-end way? IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2777–2787. https://doi.org/10.1109/TCSVT.2017.2748698
  • Wang, G., Lin, L., Ding, S., Li, Y., & Wang, Q. (2016). Dari: Distance metric and representation integration for person verification. Proceedings of the AAAI Conference on Artificial Intelligence, The Phoenix Convention Center, Phoenix, Arizona, USA, 30.
  • Wang, Y., Wang, Z., Jiang, M., Zhang, H., & Tang, E. (2021). Graph convolutional network for person re-identification based on part representation. Wireless Personal Communications, 127(S1), 1–16. https://doi.org/10.1007/s11277-021-08781-w
  • Wan, F., Wu, Y., Qian, X., & Fu, Y. (2020). When person re-identification meets changing clothes. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, Washington, USA (pp. 830–831).
  • Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018). Person transfer gan to bridge domain gap for person re-identification. Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, Utah (pp. 79–88).
  • Xu, Y., Zhao, L., & Qin, F. (2021). Dual attention-based method for occluded person re-identification. Knowledge-Based Systems, 212, 106554. https://doi.org/10.1016/j.knosys.2020.106554
  • Yang, Q., Wu, A., & Zheng, W. S. (2019). Person re-identification by contour sketch under moderate clothing change. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6), 2029–2046. https://doi.org/10.1109/TPAMI.2019.2960509
  • Yu, S., Li, S., Chen, D., Zhao, R., Yan, J., & Qiao, Y. (2020). Cocas: A large-scale clothes changing person dataset for re-identification. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA (pp. 3400–3409).
  • Yu, Z., Zhao, Y., Hong, B., Jin, Z., Huang, J., Cai, D., He, X., & Hua, X. S. (2021). Apparel-invariant feature learning for person re-identification. IEEE Transactions on Multimedia.
  • Zhang, Z., Zhang, H., Liu, S., Xie, Y., & Durrani, T. S. (2021). Part-guided graph convolution networks for person re-identification. Pattern Recognition, 120, 108155. https://doi.org/10.1016/j.patcog.2021.108155
  • Zheng, M., Karanam, S., Wu, Z., & Radke, R. J. (2019). Re-identification with consistent attentive siamese networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, California (pp. 5735–5744).
  • Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q. (2015). Scalable person re-identification: A benchmark. Proceedings of the IEEE International Conference on Computer Vision (ICCV), NW Washington, DC. United States.
  • Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J. (2019) Joint discriminative and generative learning for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2138–2147
  • Zheng, Z., Zheng, L., & Yang, Y. (2017). Unlabeled samples generated by gan improve the person re-identification baseline in vitro. Proceedings of the IEEE international conference on computer vision, Venice, Italy (pp. 3754–3762).
  • Zhong, Z., Zheng, L., Cao, D., & Li, S. (2017). Re-ranking person re-identification with k-reciprocal encoding. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii (pp. 1318–1327).
  • Zhong, Z., Zheng, L., Kang, G., Li, S., & Yi, Y. (2017). Random erasing data augmentation. Proceedings of the AAAI conference on artificial intelligence, Hilton New York Midtown, New York, New York, USA, 34(7), 13001–13008.
  • Zhuo, J., Chen, Z., Lai, J., & Wang, G. (2018). Occluded person re-identification. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Miami, Florida, USA (pp. 1–6).

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