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Using Segmentation Priors to Improve the Video Surveillance Person Re-Identification Accuracy

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Image Processing and Communications Challenges 10 (IP&C 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 892))

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Abstract

In this paper, a method for improving the quality of person re-identification results is presented. The method is based on the assumption, that including segmentation information into re-identification pipeline suppresses the influence of the background and discards the automated detections that are of poor quality due to occlusions, misplaced regions of interest (ROI), multiple persons found within a single ROI etc. Assuming that a joint detector-segmented approach is used, the additional cost associated with the use of the proposed approach is very low.

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References

  1. Abdulla, W.: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow (2017). https://github.com/matterport/Mask_RCNN

  2. Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-shot person re-identification by HPE signature. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1413–1416. IEEE (2010)

    Google Scholar 

  3. Dozat, T.: Incorporating Nesterov momentum into Adam (2015)

    Google Scholar 

  4. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2360–2367. IEEE (2010)

    Google Scholar 

  5. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988. IEEE (2017)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)

  7. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  8. Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. arXiv preprint arXiv:1705.04724 (2017)

  9. Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)

    Google Scholar 

  10. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1137–1149 (2017)

    Article  Google Scholar 

  11. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Deep metric learning for person re-identification. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 34–39. IEEE (2014)

    Google Scholar 

  12. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)

    Google Scholar 

  13. Zajdel, W., Zivkovic, Z., Krose, B.: Keeping track of humans: have I seen this person before? In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation 2005, ICRA 2005, pp. 2081–2086. IEEE (2005)

    Google Scholar 

  14. Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: MARS: a video benchmark for large-scale person re-identification. In: European Conference on Computer Vision, pp. 868–884. Springer (2016)

    Google Scholar 

  15. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

    Google Scholar 

  16. Zheng, Z., Zheng, L., Yang, Y.: Pedestrian alignment network for large-scale person re-identification. arXiv preprint arXiv:1707.00408 (2017)

  17. Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3652–3661. IEEE (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Control, Robotics and Information Engineering, Poznań University of Technology, Piotrowo 3A, 60-965, Poznań, Poland

    Dominik Pieczyński, Marek Kraft & Michał Fularz

Authors
  1. Dominik Pieczyński
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  2. Marek Kraft
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  3. Michał Fularz
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Corresponding author

Correspondence to Marek Kraft .

Editor information

Editors and Affiliations

  1. Institute of Telecommunications and Computer Science, University of Science and Technology, UTP, Bydgoszcz, Poland

    Michał Choraś

  2. Institute of Telecommunications and Computer Science, University of Science and Technology, UTP, Bydgoszcz, Poland

    Ryszard S. Choraś

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Pieczyński, D., Kraft, M., Fularz, M. (2019). Using Segmentation Priors to Improve the Video Surveillance Person Re-Identification Accuracy. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications Challenges 10. IP&C 2018. Advances in Intelligent Systems and Computing, vol 892. Springer, Cham. https://doi.org/10.1007/978-3-030-03658-4_16

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