Skip to main content
SpringerLink
Log in
Book cover

Deep Learning: Algorithms and Applications pp 263–297Cite as

Deep Learning for Person Re-identification in Surveillance Videos

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 865))

Abstract

In the recent years, Closed Circuit Television (CCTV) is viewed as the basis for providing security. One of the most important aspects of CCTV surveillance systems security mechanism is to re-identify a person captured in one of the camera across different surveillance cameras. Re-identification has a major role in several applications like automated surveillance of universities, offices, malls, home and restricted environments like embassies or laboratories with strong security restrictions. Traditionally, identifying a person in a video was practiced under the set of same external conditions (like same illumination, viewpoint, back ground conditions etc.). But when it comes to automated re-identification in a CCTV surveillance system, several challenges emerge as the environment is uncontrolled and keeps varying, further the poses of the person and the angles of the cameras capturing the videos also incur additional challenge for the task considered. When a person disappears from one camera view for a period of time, he should be recognized in another view of camera at a different location when there are environmental disturbances like variation in illumination, crowded scene, partial occlusions, physical appearance variations, full occlusions, view point variations, background clutter, shadows and reflections, etc. In this chapter, the major focus is on the techniques of deep learning used to develop an end-to-end re-identification system highlighting the methods to handle the uncontrolled environment challenges mentioned. An end-to-end re-identification task consists of sequence of steps namely pedestrian detection, person tracking followed by person re-identification. Given a video sequence or an image as an input, firstly the humans are detected from the video sequence as a process of pedestrian detection. The person tracking within the camera is conducted, to find the different poses of the probe if needed. Then the re-identification process is conducted where the deep learning models are used to re-identify the person with the help of gallery set of videos and evaluates the similarities of gallery set and the person of interest by using deep learning metrics. The re-identification results end as a retrieval process where all similar images of the person of interest are retrieved. Several bench mark datasets considered in literature for re-identification system are VIPeR, ETHZ, PRID, CAVIAR, CUHK01, CUHK02, CUHK03, i-LIDS, RAiD, MARS, etc.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270–286 (2014)

    Article  Google Scholar 

  2. Zheng, L., Yang, Y., Hauptmann, A. G.: Person re-identification: Past, present and future (2016). arXiv preprint arXiv:1610.02984

  3. Saghafi, M.A., Hussain, A., Zaman, H.B., Saad, M.H.M.: Review of person re-entification techniques. IET Comput. Vision 8(6), 455–474 (2014)

    Article  Google Scholar 

  4. Zajdel, W., Zivkovic, Z., Krose, B.J.A.: 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 

  5. Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Null, pp. 1528–1535. IEEE (2006)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  8. Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159 (2014)

    Google Scholar 

  9. Xu, Y., Ma, B., Huang, R., Lin, L.: Person search in a scene by jointly modeling people commonness and person uniqueness. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 937–940. ACM (2014)

    Google Scholar 

  10. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: ISCAS Vol. 2010, pp. 253–256 (2010)

    Google Scholar 

  11. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  12. Charu, C.A.: Neural Networks and Deep Learning: A Textbook. Springer (2019)

    Google Scholar 

  13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  14. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833. Springer, Cham (2014)

    Google Scholar 

  15. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

  16. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.,… Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  17. Dauphin, Y.N., de Vries, H., Chung, J., Bengio, Y.: RMSProp and equilibrated adaptive learning rates for non-convex optimization. CoRR arXiv:1502.04390 (2015)

  18. He, K., Zhang, X., RenSchustera, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  19. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Google Scholar 

  20. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  21. Graves, A.: Supervised sequence labelling. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 5–13. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  22. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  23. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. (1994)

    Google Scholar 

  24. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  25. Shanmugamani, R.: Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras. Packt Publishing Ltd (2018)

    Google Scholar 

  26. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks (2013). arXiv preprint arXiv:1302.4389

  27. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus) (2015). arXiv preprint arXiv:1511.07289

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

    Google Scholar 

  29. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Null, pp. 1735–1742. IEEE (2006)

    Google Scholar 

  30. McLaughlin, N., Martinez del Rincon, J., Miller, P.: Recurrent convolutional network for video-based person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1325–1334 (2016)

    Google Scholar 

  31. Chung, D., Tahboub, K., Delp, E.J.: A two stream siamese convolutional neural network for person re-identification. In: The IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  32. Wu, L., Wang, Y., Li, X., Gao, J.: What-and-where to match: deep spatially multiplicative integration networks for person re-identification. Pattern Recogn. 76, 727–738 (2018)

    Article  Google Scholar 

  33. Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, pp. 262–275. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  34. Ess, A., Leibe, B., Van Gool, L.: Depth and appearance for mobile scene analysis. In: 11th International Conference on Computer Vision, 2007. ICCV 2007. IEEE, pp. 1–8. IEEE (2007)

    Google Scholar 

  35. Loy, C.C., Xiang, T., Gong, S.: Time-delayed correlation analysis for multi-camera activity understanding. Int. J. Comput. Vision 90(1), 106–129 (2010)

    Article  Google Scholar 

  36. Zheng, W., Gong, S., Xiang., T.: Associating groups of people. In: BMVC (2009)

    Google Scholar 

  37. Baltieri, D., Vezzani, R., Cucchiara, R.: 3dpes: 3d people dataset for surveillance and forensics. In: Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, pp. 59–64. ACM (2011)

    Google Scholar 

  38. Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: Bmvc, Vol. 1, No. 2, p. 6 (2011)

    Google Scholar 

  39. Hirzer, M., Beleznai, C., Roth, P. M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Scandinavian Conference on Image Analysis, pp. 91–102. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  40. Bialkowski, A., Denman, S., Sridharan, S., Fookes, C., Lucey, P.: A database for person re-identification in multi-camera surveillance networks. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1–8. IEEE (2012)

    Google Scholar 

  41. Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision, pp. 31–44. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  42. Martinel, N., Micheloni, C.: Re-identify people in wide area camera network. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2012, pp. 31–36. IEEE (2012)

    Google Scholar 

  43. Li, W., Wang, X.: Locally aligned feature transforms across views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3594–3601 (2013)

    Google Scholar 

  44. Wang, T., Gong, S., Zhu, X., Wang, S.: Person re-identification by video ranking. In: European Conference on Computer Vision, pp. 688–703. Springer, Cham (2014)

    Google Scholar 

  45. Branch, H.O.S.D.: Imagery library for intelligent detection systems (i-lids). In: The Institution of Engineering and Technology Conference on Crime and Security, pp. 445–448 (2006)

    Google Scholar 

  46. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  47. Das, A., Chakraborty, A., Roy-Chowdhury, A.K.: Consistent re-identification in a camera network. In: European Conference on Computer Vision, pp. 330–345. Springer, Cham (2014)

    Google Scholar 

  48. 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 

  49. Ma, L., Liu, H., Hu, L., Wang, C., Sun, Q.: Orientation driven bag of appearances for person re-identification (2016). arXiv preprint arXiv:1605.02464

  50. 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, Cham (2016)

    Google Scholar 

  51. Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1367–1376(2017)

    Google Scholar 

  52. Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: End-to-end deep learning for person search (2016). arXiv preprint arXiv:1604.01850, 1(2)

  53. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision, pp. 17–35. Springer, Cham (2016)

    Google Scholar 

  54. Camps, O., Gou, M., Hebble, T., Karanam, S., Lehmann, O., Li, Y.,… Xiong, F.: From the lab to the real world: Re-identification in an airport camera network. IEEE Trans. Circuits Syst. Video Technol. 27(3), 540–553 (2017)

    Google Scholar 

  55. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)

    Google Scholar 

  56. Zheng, M., Karanam, S., Radke, R.J.: RPIfield: a new dataset for temporally evaluating person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1893–1895 (2018)

    Google Scholar 

  57. Zhang, G., Kato, J., Wang, Y., Mase, K.: People re-identification using deep convolutional neural network. In: Computer Vision Theory and Applications (VISAPP), 2014 International Conference on Vol. 3, pp. 216–223. IEEE (2014)

    Google Scholar 

  58. Ahmed, E., Jones, M., Marks, T.K.: An improved deep learning architecture for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3908–3916 (2015)

    Google Scholar 

  59. Ding, S., Lin, L., Wang, G., Chao, H.: Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn. 48(10), 2993–3003 (2015)

    Article  Google Scholar 

  60. Zhang, R., Lin, L., Zhang, R., Zuo, W., Zhang, L.: Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)

    Article  MathSciNet  Google Scholar 

  61. Shi, H., Zhu, X., Liao, S., Lei, Z., Yang, Y., Li, S.Z.: Constrained deep metric learning for person re-identification (2015). arXiv preprint arXiv:1511.07545

  62. Iodice, S., Petrosino, A., Ullah, I.: Strict pyramidal deep architectures for person re-identification. In: International Workshop on Neural Networks, pp. 179–186. Springer, Cham (2015)

    Google Scholar 

  63. Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1335–1344 (2016)

    Google Scholar 

  64. Chen, S.Z., Guo, C.C., Lai, J.H.: Deep ranking for person re-identification via joint representation learning. IEEE Trans. Image Process. 25(5), 2353–2367 (2016)

    Article  MathSciNet  Google Scholar 

  65. Wu, S., Chen, Y.C., Li, X., Wu, A.C., You, J.J., Zheng, W.S.: An enhanced deep feature representation for person re-identification. In: Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on, pp. 1–8. IEEE (2016)

    Google Scholar 

  66. Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1249–1258 (2016)

    Google Scholar 

  67. Wu, L., Shen, C., Hengel, A.V.D.: Personnet: Person re-identification with deep convolutional neural networks (2016). arXiv preprint arXiv:1601.07255

  68. Li, S., Liu, X., Liu, W., Ma, H., Zhang, H.: A discriminative null space based deep learning approach for person re-identification. In: 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), 2016, pp. 480–484. IEEE (2016)

    Google Scholar 

  69. Shi, H., Yang, Y., Zhu, X., Liao, S., Lei, Z., Zheng, W., Li, S.Z.: Embedding deep metric for person re-identification: A study against large variations. In: European Conference on Computer Vision, pp. 732–748. Springer, Cham (2016)

    Google Scholar 

  70. Varior, R.R., Shuai, B., Lu, J., Xu, D., Wang, G.: A siamese long short-term memory architecture for human re-identification. In: European Conference on Computer Vision, pp. 135–153. Springer, Cham (2016)

    Google Scholar 

  71. Wang, F., Zuo, W., Lin, L., Zhang, D., Zhang, L.: Joint learning of single-image and cross-image representations for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1288–1296 (2016)

    Google Scholar 

  72. Franco, A., Oliveira, L.: A coarse-to-fine deep learning for person re-identification. In: IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1–7. IEEE (2016)

    Google Scholar 

  73. McLaughlin, N., del Rincon, J.M., Miller, P.C.: Person reidentification using deep convnets with multitask learning. IEEE Trans. Circuits Syst. Video Techn. 27(3), 525–539 (2017)

    Article  Google Scholar 

  74. Liu, J., Zha, Z.J., Tian, Q.I., Liu, D., Yao, T., Ling, Q., Mei, T.: Multi-scale triplet cnn for person re-identification. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 192–196. ACM (2016)

    Google Scholar 

  75. Wang, J., Wang, Z., Gao, C., Sang, N., Huang, R.: DeepList: learning deep features with adaptive Listwise constraint for person reidentification. IEEE Trans. Circuits Syst. Video Techn. 27(3), 513–524 (2017)

    Article  Google Scholar 

  76. Liu, H., Feng, J., Qi, M., Jiang, J., Yan, S.: End-to-end comparative attention networks for person re-identification. IEEE Trans. Image Process. 26(7), 3492–3506 (2017)

    Article  MathSciNet  Google Scholar 

  77. Wu, L., Shen, C., van den Hengel, A.: Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification. Pattern Recogn. 65, 238–250 (2017)

    Article  Google Scholar 

  78. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3980–3989. IEEE (2017)

    Google Scholar 

  79. Franco, A., Oliveira, L.: Convolutional covariance features: Conception, integration and performance in person re-identification. Pattern Recogn. 61, 593–609 (2017)

    Article  Google Scholar 

  80. Qian, X., Fu, Y., Jiang, Y.G., Xiang, T., Xue, X.: Multi-scale deep learning architectures for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5399–5408 (2017)

    Google Scholar 

  81. Zhu, J., Zeng, H., Liao, S., Lei, Z., Cai, C., Zheng, L.: Deep hybrid similarity learning for person re-identification. IEEE Trans. Circuits Syst. Video Technol. 28(11), 3183–3193 (2018)

    Article  Google Scholar 

  82. Cheng, D., Gong, Y., Chang, X., Shi, W., Hauptmann, A., Zheng, N.: Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification. Pattern Recogn. (2018)

    Google Scholar 

  83. Mao, C., Li, Y., Zhang, Z., Zhang, Y., Li, X.: Pyramid Person Matching Network for Person Re-identification(2018). arXiv preprint arXiv:1803.02547

  84. Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 384–393 (2017)

    Google Scholar 

  85. Lin, J., Ren, L., Lu, J., Feng, J., Zhou, J.: Consistent-aware deep learning for person re-identification in a camera network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 6 (2017)

    Google Scholar 

  86. Bai, X., Yang, M., Huang, T., Dou, Z., Yu, R., Xu, Y.: Deep-Person: Learning Discriminative Deep Features for Person Re-Identification (2017). arXiv preprint arXiv:1711.10658

  87. Chang, Y. S., Wang, M.Y., He, L., Lu, W., Su, H., Gao, N., Yang, X.A.: Joint deep semantic embedding and metric learning for person re-identification. Pattern Recogn. Lett. (2018)

    Google Scholar 

  88. Chen, Y., Duffner, S., Stoian, A., Dufour, J.Y., Baskurt, A.: Deep and low-level feature-based attribute learning for person re-identification. Image Vis. Comput. 79, 25–34 (2018)

    Article  Google Scholar 

  89. Tao, D., Guo, Y., Yu, B., Pang, J., Yu, Z.: Deep multi-view feature learning for person re-identification. IEEE Trans. Circuits Syst. Video Technol. 28(10), 2657–2666 (2018)

    Article  Google Scholar 

  90. Wu, L., Wang, Y., Ge, Z., Hu, Q., Li, X.: Structured deep hashing with convolutional neural networks for fast person re-identification. Comput. Vis. Image Underst. 167, 63–73 (2018)

    Article  Google Scholar 

  91. Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q.: Multi-type attributes driven multi-camera person re-identification. Pattern Recogn. 75, 77–89 (2018)

    Article  Google Scholar 

  92. Wang, J., Zhou, S., Wang, J., Hou, Q.: Deep ranking model by large adaptive margin learning for person re-identification. Pattern Recogn. 74, 241–252 (2018)

    Article  Google Scholar 

  93. Wu, D., Zheng, S.J., Yuan, C.A., Huang, D.S.: A deep model with combined losses for person re-identification. Cogn. Syst. Res. 54, 74–82 (2019)

    Article  Google Scholar 

  94. Zhang, Z., Si, T., Liu, S.: Integration convolutional neural network for person re-identification in camera networks. IEEE Access 6, 36887–36896 (2018)

    Article  Google Scholar 

  95. Liu, Y., Song, N., Han, Y.: Multi-cue fusion: Discriminative enhancing for person re-identification. J. Vis. Commun. Image Represent. 58, 46–52 (2019)

    Article  Google Scholar 

  96. Wang, F., Zhang, C., Chen, S., Ying, G., Lv, J.: Engineering Hand-designed and Deeply-learned features for person Re-identification. Pattern Recogn. Lett. (2018)

    Google Scholar 

  97. Yuan, C., Guo, J., Feng, P., Zhao, Z., Xu, C., Wang, T., … & Duan, K.: A jointly learned deep embedding for person re-identification. Neurocomputing 330, 127–137 (2019)

    Google Scholar 

  98. Xin, X., Wang, J., Xie, R., Zhou, S., Huang, W., Zheng, N.: Semi-supervised person Re-Identification using multi-view clustering. Pattern Recogn. 88, 285–297 (2019)

    Article  Google Scholar 

  99. Wu, D., Zheng, S.J., Bao, W.Z., Zhang, X.P., Yuan, C.A., Huang, D.S.: A novel deep model with multi-loss and efficient training for person re-identification. Neurocomputing 324, 69–75 (2019)

    Article  Google Scholar 

  100. Zhou, S., Ke, M., Luo, P.: Multi-camera transfer GAN for person re-identification. J. Vis. Commun. Image Represent. (2019)

    Google Scholar 

  101. Zhong, W., Jiang, L., Zhang, T., Ji, J., Xiong, H.: Combining multilevel feature extraction and multi-loss learning for person re-identification. Neurocomputing (2019)

    Google Scholar 

  102. Fumera, B.L.G., Roli, F.: Multi-stage ranking approach for fast person re-identification. IET Comput. Vis. 12(4), 513–519 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank VIT for providing ‘VIT SEED GRANT’ for carrying out this research work. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research on person Re-identification.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT, Vellore, 632014, India

    Swathi Jamjala Narayanan, Boominathan Perumal, Sangeetha Saman & Aditya Pratap Singh

Authors
  1. Swathi Jamjala Narayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boominathan Perumal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sangeetha Saman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aditya Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swathi Jamjala Narayanan .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Narayanan, S.J., Perumal, B., Saman, S., Singh, A.P. (2020). Deep Learning for Person Re-identification in Surveillance Videos. In: Pedrycz, W., Chen, SM. (eds) Deep Learning: Algorithms and Applications. Studies in Computational Intelligence, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-030-31760-7_9

Download citation

Publish with us

Policies and ethics

Search

Navigation