Person Re-Identification Network Based on Edge-Enhanced Feature Extraction and Inter-Part Relationship Modeling
Source abstract
Abstract
Person re-identification (Re-ID) is a technique for identifying target pedestrians in images or videos. In recent years, owing to the advancements in deep learning, research on person re-identification has made significant progress. However, current methods mostly focus on salient regions within the entire image, overlooking certain hidden features specific to pedestrians themselves. Motivated by this consideration, we propose a novel person re-identification network. Our approach integrates pedestrian edge features into the representation and utilizes edge information to guide global context feature extraction. Additionally, by modeling the internal relationships between different parts of pedestrians, we enhance the network’s ability to capture and understand the interdependencies within pedestrians, thereby improving the semantic coherence of pedestrian features. Ultimately, by fusing these multifaceted features, we generate comprehensive and highly discriminative representations of pedestrians, significantly enhancing person Re-ID performance. Experimental results demonstrate that our method outperforms most state-of-the-art approaches in person re-identification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Gait Recognition and Analysis · Face recognition and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200201, China
References (60)
Show source-provided references
1. Farenzena, M., Bazzani, L., Perina, A., Murino, V., and Cristani, M. (2010, January 13–18). Person re-identification by symmetry-driven accumulation of local features. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.10.1109/CVPR.2010.5539926 ↗
2. Hirzer, M., Beleznai, C., Roth, P.M., and Bischof, H. (2011, January 1). Person re-identification by descriptive and discriminative classification. Proceedings of the 17th Scandinavian Conference on Image Analysis (SCIA’11), Ystad, Sweden.10.1007/978-3-642-21227-7_9 ↗
3. Li, W., Zhao, R., and Wang, X. (2012, January 5–9). Human reidentification with transferred metric learning. Proceedings of the 11th Asian Conference on Computer Vision, Daejeon, Republic of Korea.
4. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
5. Simonyan, K., and Zisserman, A. (2024, January 7–11). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 12th International Conference on Learning Representations, Vienna, Austria.
6. Sun, Y., Zheng, L., Yang, Y., Tian, Q., and Wang, S. (2018, January 8–14). Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline). Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.10.1007/978-3-030-01225-0_30 ↗
7. Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7–12). Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.
8. Multi-scale feature fusion network for person re-identification10.1049/iet-ipr.2020.0008 ↗
9. Attribute-Guided Global and Part-Level Identity Network for Person Re-Identification10.1142/S0218001422500112 ↗
10. Zhong, Z., Zheng, L., Cao, D., and Li, S. (2017, January 21–26). Re-ranking Person Re-identification with k-Reciprocal Encoding. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.389 ↗
11. Re-ranking with ranking-reflected similarity for person re-identification10.1016/j.patrec.2019.09.020 ↗
12. Song, C., Huang, Y., Ouyang, W., and Wang, L. (2018, January 18–23). Mask-Guided Contrastive Attention Model for Person Re-identification. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00129 ↗
13. Li, W., Zhu, X., and Gong, S. (2017, January 19–25). Person re-identification by deep joint learning of multi-loss classification. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia.10.24963/ijcai.2017/305 ↗
14. Two-stream person re-identification with multi-task deep neural networks10.1007/s00138-018-0915-1 ↗
15. Ghorbel, M., Ammar, S., Kessentini, Y., Jmaiel, M., and Chaari, A. (2020, January 20–22). Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks. Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence, Hammamet, Tunisia.10.1007/978-3-030-71804-6_6 ↗
16. Saber, S., Meshoul, S., Amin, K., Pławiak, P., and Hammad, M. (2023). A Multi-Attention Approach for Person Re-Identification Using Deep Learning. Sensors, 23.10.3390/s23073678 ↗
17. Learning global and local features using graph neural networks for person re-identification10.1016/j.image.2022.116744 ↗
18. Guo, T., Wang, D., Jiang, Z., Men, A., and Zhou, Y. (2018, January 9–12). Deep Network with Spatial and Channel Attention for Person Re-identification. Proceedings of the 2018 IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan.10.1109/VCIP.2018.8698620 ↗
19. Multi-level attention model for person re-identification10.1016/j.patrec.2018.08.024 ↗
20. Zhang, Z., Lan, C., Zeng, W., Jin, X., and Chen, Z. (2020, January 13–19). Relation-Aware Global Attention for Person Re-Identification. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.00325 ↗
21. Zhang, Z., Zhang, H., and Liu, S. (2021, January 20–25). Person Re-identification using Heterogeneous Local Graph Attention Networks. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01196 ↗
22. Learning Multi-Attention Context Graph for Group-Based Re-Identification10.1109/TPAMI.2020.3032542 ↗
23. Part-guided graph convolution networks for person re-identification10.1016/j.patcog.2021.108155 ↗
24. Multi-task person re-identification via attribute and part-based learning10.1007/s11042-022-12124-7 ↗
25. Cho, Y.H., Kim, W.J., Hong, S., and Yoon, S. (2022, January 18–24). Part-based Pseudo Label Refinement for Unsupervised Person Re-identification. Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.10.1109/CVPR52688.2022.00716 ↗
26. Part-Based Representation Enhancement for Occluded Person Re-Identification10.1109/TCSVT.2023.3241764 ↗
27. Qi, L., Huo, J., Wang, L., Shi, Y., and Gao, Y. (2019, January 8–12). A Mask Based Deep Ranking Neural Network for Person Retrieval. Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China.10.1109/ICME.2019.00092 ↗
28. Mohammed, H.J., Al-Fahdawi, S., Al-Waisy, A.S., Zebari, D.A., Ibrahim, D.A., Mohammed, M.A., Kadry, S., and Kim, J. (2022). ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification. Mathematics, 10.10.3390/math10193530 ↗
29. Fully Convolutional Networks for Semantic Segmentation10.1109/TPAMI.2016.2572683 ↗
30. A Computational Approach to Edge Detection10.1109/TPAMI.1986.4767851 ↗
31. Khan, R., Islam, S., Kanwal, K., Iqbal, M., Hossain, M.I., and Ye, Z.F. (2022). A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism. arXiv.
32. Yang, S., Yu, X., and Zhou, Y. (2020, January 12–14). LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. Proceedings of the 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), Shanghai, China.10.1109/IWECAI50956.2020.00027 ↗
33. Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7–12). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298682 ↗
34. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. (2015, January 7–13). Scalable person re-identification: A benchmark. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.133 ↗
35. Ristani, E., Solera, F., Zou, R., Cucchiara, R., and Tomasi, C. (15–16, January 8–10). Performance measures and a data set for multi-target, multi-camera tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.
36. Li, W., Zhao, R., Xiao, T., and Wang, X. (2014, January 23–28). DeepReID: Deep filter pairing neural network for person re-identification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.27 ↗
37. Neighboring-Part Dependency Mining and Feature Fusion Network for Person Re-Identification10.1109/ACCESS.2023.3274473 ↗
38. Wang, C., Zhang, Q., Huang, C., Liu, W., and Wang, X. (2018, January 8–14). Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-Identification. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.10.1007/978-3-030-01225-0_23 ↗
39. Li, W., Zhu, X., and Gong, S. (2018, January 18–22). Harmonious Attention Network for Person Re-identification. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00243 ↗
40. Chen, B., Deng, W., and Hu, J. (2019, January 27). Mixed High-Order Attention Network for Person Re-Identification. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00046 ↗
41. Fang, P., Zhou, J., Roy, S., Petersson, L., and Harandi, M. (2019, January 27). Bilinear Attention Networks for Person Retrieval. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00812 ↗
42. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., and Tian, Q. (2017, January 22–29). Pose-Driven Deep Convolutional Model for Person Re-identification. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.427 ↗
43. GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification10.1109/TMM.2018.2870522 ↗
44. Part-Based Attribute-Aware Network for Person Re-Identification10.1109/ACCESS.2019.2912844 ↗
45. Li, L., Dang, J., Wang, Y., Wang, S., and Zhang, Z. (2019, January 18–21). Part-Based Bilinear CNN For Person Re-Identification. Proceedings of the 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China.10.1109/APSIPAASC47483.2019.9023340 ↗
46. Tay, C.-P., Roy, S., and Yap, K.-H. (2019, January 15–20). AANet: Attribute Attention Network for Person Re-Identifications. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00730 ↗
47. Zhang, B., Li, Y., Chen, H., and Sun, J. (2020, January 15–17). Improving Person Re-identification by Mask Guiding and Part Pooling. Proceedings of the 2020 12th International Conference on Machine Learning and Computing (ICMLC ‘20), Shenzhen, China.10.1145/3383972.3383993 ↗
48. Mask-guided class activation mapping network for person re-identification10.1049/el.2020.1643 ↗
49. An End-to-End Foreground-Aware Network for Person Re-Identification10.1109/TIP.2021.3050839 ↗
50. Improving person re-identification by attribute and identity learning10.1016/j.patcog.2019.06.006 ↗
51. Luo, J., Liu, Y., Gao, C., and Sang, N. (2019, January 22–25). Learning What and Where from Attributes to Improve Person Re-Identification. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.10.1109/ICIP.2019.8802961 ↗
52. Attributes-aided part detection and refinement for person re-identification10.1016/j.patcog.2019.107016 ↗
53. Sun, Y., Zheng, L., Deng, W., and Wang, S. (2017, January 22–29). SVDNet for Pedestrian Retrieval. Proceedings of the 2017 IEEE International Con-ference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.410 ↗
54. Pedestrian Alignment Network for Large-scale Person Re-Identification10.1109/TCSVT.2018.2873599 ↗
55. Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., and Chen, X. (2019, January 15–20). Interaction-And-Aggregation Network for Person Re-Identification. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00954 ↗
56. Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., and Kautz, J. (2019, January 15–20). Joint Discriminative and Generative Learning for Person Re-Identification. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00224 ↗
57. Zhou, K., Yang, Y., Cavallaro, A., and Xiang, T. (2019, January 27). Omni-Scale Feature Learning for Person Re-Identification. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.10.1109/ICCV.2019.00380 ↗
58. Jin, X., Lan, C., Zeng, W., Wei, G., and Chen, Z. (2020, January 7–12). Semantics-Aligned Representation Learning for Person Re-identification. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6775 ↗
59. He, L., and Liu, W. (2020, January 23–28). Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK.10.1007/978-3-030-58604-1_22 ↗
60. Zhou, Y., Qin, Y., Wang, S., Huang, Z., and Zhang, D. (2023, January 18–20). Person Re-Identification Based on Instance Segmentation and Pose Estimation. Proceedings of the 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Haikou, China.10.1109/PRAI59366.2023.10332122 ↗