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Person Search via Anchor-Free Detection and Part-Based Group Feature Similarity Estimation

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12306))

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Abstract

In order to solve the problems of insufficient accuracy of pedestrian bounding boxes in person search and large-scale person matching. A novel person search framework is proposed, which includes: (1) A multi-layer cascade heatmap mechanism (MCHM) is proposed, which aggregates pedestrian features by multi-layer heatmaps cascaded and improves the accuracy of the pedestrian bounding box by optimizating the offset between the center of the bounding box and the center point. (2) A learnable part-based pedestrian feature weight calculation module is proposed, which can learn the weight of the part according to the importance of the part-based feature instead of manually set hyperparameters. (3) A group feature correlation graph convolution network (GFCGCN) is proposed, which can calculate the similarity between group pedestrian features and provide a more accuracy end to end person search work. Some ablation studies and comparative experiments on datasets CUHK-SYSU, PRW show that our model can effectively achieve more accuracy pearch search with accuracy of 88.7% rank-1 and 78.2% mAP.

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References

  1. Wu, D., Zhang, K., Zheng, S.J., et al.: Random occlusion recovery for person re-identification. J. Imaging Sci. Technol. 63(3), 30405-1–30405-9 (2019)

    Google Scholar 

  2. Wu, Q., Dai, P., Chen, P., et al.: Deep adversarial data augmentation with attribute guided for person re-identification. Signal Image Video Process. 1–8 (2019). https://doi.org/10.1007/s11760-019-01523-3

  3. Liu, H., Feng, J., Jie, Z., et al.: Neural person search machines. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 493–501 (2017)

    Google Scholar 

  4. Zheng, L., Zhang, H., Sun, S., et al.: Person re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1367–1376 (2017)

    Google Scholar 

  5. Guo, S., Bai, Q., Zhou, X.: Foreign object detection of transmission lines based on faster R-CNN. In: Kim, K.J., Kim, H.-Y. (eds.) Information Science and Applications. LNEE, vol. 621, pp. 269–275. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1465-4_28

    Chapter  Google Scholar 

  6. Lin, T.Y., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  7. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

    Google Scholar 

  8. Durkee, M.S., Sibley, A., Ai, J., et al.: Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN. In: Medical Imaging 2020: Digital Pathology, vol. 11320, p. 1132019. International Society for Optics and Photonics (2020)

    Google Scholar 

  9. Jiang, H., Li, S., Liu, W., et al.: Geometry-aware cell detection with deep learning. MSystems 5(1) (2020)

    Google Scholar 

  10. Hasan, I., Tsesmelis, T., Galasso, F., et al.: Tiny head pose classification by bodily cues. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2662–2666. IEEE (2017)

    Google Scholar 

  11. Xiao, J., Xie, Y., Tillo, T., et al.: IAN: the individual aggregation network for person search. Pattern Recogn. 87, 332–340 (2019)

    Article  Google Scholar 

  12. Jiang, M., Li, C., Kong, J., et al.: Cross-level reinforced attention network for person re-identification. J. Vis. Commun. Image Represent. 102775 (2020)

    Google Scholar 

  13. Şerbetçi, A., Akgül, Y.S.: End-to-end training of CNN ensembles for person re-identification. Pattern Recognit. 107319 (2020)

    Google Scholar 

  14. Zhao, C., Lv, X., Zhang, Z., et al.: Deep fusion feature representation learning with hard mining center-triplet loss for person re-identification. IEEE Trans. Multimedia (2020)

    Google Scholar 

  15. Zhang, C., Yue, J., Qin, Q.: Deep quadruplet network for hyperspectral image classification with a small number of samples. Remote Sens. 12(4), 647 (2020)

    Article  Google Scholar 

  16. Ye, M., Shen, J., Lin, G., et al.: Deep Learning for Person Re-identification: A Survey and Outlook. arXiv preprint arXiv:2001.04193 (2020)

  17. Xiao, T., Li, S., Wang, B., et al.: Joint detection and identification feature learning for person search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3415–3424 (2017)

    Google Scholar 

  18. Zhu, X., Chen, C., Zheng, B., et al.: Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN. Biosyst. Eng. 189, 116–132 (2020)

    Article  Google Scholar 

  19. Mai, X., Zhang, H., Jia, X., et al.: Faster R-CNN with classifier fusion for automatic detection of small fruits. IEEE Trans. Autom. Sci. Eng. (2020)

    Google Scholar 

  20. Zhou, J., Chen, B., Zhang, J., et al.: Multi-scales feature integration single shot multi-box detector on small object detection. In: MIPPR 2019: Pattern Recognition and Computer Vision, vol. 11430, p. 114300E. International Society for Optics and Photonics (2020)

    Google Scholar 

  21. Law, H., Deng, J.: Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018)

    Google Scholar 

  22. Duan, K., Bai, S., Xie, L., et al.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6569–6578 (2019)

    Google Scholar 

  23. Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: CVPR, pp. 3586–3593 (2013)

    Google Scholar 

  24. Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR, pp. 2288–2295 (2012)

    Google Scholar 

  25. Ktena, S.I., et al.: Distance metric learning using graph convolutional networks: application to functional brain networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 469–477. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_54

    Chapter  Google Scholar 

  26. He, Z., Zhang, L.: End-to-end detection and re-identification integrated net for person search. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11362, pp. 349–364. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20890-5_23

    Chapter  Google Scholar 

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Acknowledgments

This research is supported by National Natural Science Foundation of China (61972183, 61672268) and National Engineering Laboratory Director Foundation of Big Data Application for Social Security Risk Perception and Prevention.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Qing Liu, Keyang Cheng & Bin Wu

Authors
  1. Qing Liu
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  2. Keyang Cheng
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  3. Bin Wu
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Corresponding author

Correspondence to Keyang Cheng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Liu, Q., Cheng, K., Wu, B. (2020). Person Search via Anchor-Free Detection and Part-Based Group Feature Similarity Estimation. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-60639-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60638-1

  • Online ISBN: 978-3-030-60639-8

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