Skip to main content
SpringerLink
Log in

Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

Included in the following conference series:

Abstract

With the rapid development of multimedia technology and vast demand on video investigation, long-term cross-camera object tracking is increasingly important in the practical surveillance scene. Because the conventional Paired Cameras based Person Re-identification (PCPR) cannot fully satisfy the above requirement, a new framework named Camera Network based Person Re-identification (CNPR) is introduced. Two phenomena have been investigated and explored in this paper. First, the same person cannot simultaneously appear in two non-overlapping cameras. Second, the closer two cameras, the more relevant they are, in the sense that persons can transit between them with a high probability. Based on these two phenomena, a probabilistic method is proposed with reference to both visual difference and spatial-temporal constraint, to address the novel CNPR problem. (i) Spatial-temporal constraint is utilized as a filter to narrow the search space for the specific query object, and then the Weibull Distribution is exploited to formulate the spatial-temporal probability indicating the possibility of pedestrians walking to a certain camera at a certain time. (ii) Spatial-temporal probability and visual feature probability are collaborated to generate the ranking list. (iii) The multiple camera relations related to the transitions are explored to further optimize the obtained ranking list. Quantitative experiments conducted on TMin and CamNeT datasets have shown that the proposed method achieves a better performance to the novel CNPR problem.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Notes

  1. 1.

    Here, we set \(\alpha \) = 0.5, \(\lambda \) = 75, \(\beta \) = 2.5, and K = 5, when evaluating on TMin data set.

  2. 2.

    Here, we set \(\alpha \) = 0.5, \(\lambda \) = 50, \(\beta \) = 1.5, and K = 10, when evaluating on CamNeT data set.

References

  1. Gong, S., Cristami, M., Yan, S., Loy, C.: Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London (2014)

    Book  MATH  Google Scholar 

  2. Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K.: Region-based interactive ranking optimization for person re-identification. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 1–10. Springer, Heidelberg (2014)

    Google Scholar 

  3. Wang, Z., Hu, R., Liang, C., Jiang, J., Sun, K., Leng, Q., Huang, B.: Person re-identification using data-driven metric adaptation. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015, Part II. LNCS, vol. 8936, pp. 195–207. Springer, Heidelberg (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: Computer Vision and Pattern Recognition (CVPR) (2010)

    Google Scholar 

  5. Hu, Y., Liao, S., Lei, Z., Yi, D., Li, S.Z.: Exploring structural information and fusing multiple features for person re-identification. In: IEEE Workshop on Camera Networks and Wide Area Scene Analysis (in conjunction with CVPR 2013) (2013)

    Google Scholar 

  6. Gheissari, N., Sebastian, T.B., Hartley, R.: Person re-identification using spatiotemporal appearance. In: Computer Vision and Pattern Recognition (CVPR) (2006)

    Google Scholar 

  7. Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.H.: Shape and appearance context modeling. In: International Conference on Computer Vision (ICCV) (2007)

    Google Scholar 

  8. Prosser, B., Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: British Machine Vision Conference (BMVC) (2010)

    Google Scholar 

  9. Zheng, W.S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Computer Vision and Pattern Recognition (CVPR) (2011)

    Google Scholar 

  10. Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: Computer Vision and Pattern Recognition (CVPR) (2012)

    Google Scholar 

  11. Ma, L., Yang, X., Tao, D.: Person re-identification over camera networks using multi-task distance metric learning. IEEE Trans. Image Process. (TIP) 23(8), 3656–3670 (2014)

    Article  MathSciNet  Google Scholar 

  12. Das, A., Chakraborty, A., Roy-Chowdhury, A.K.: Consistent re-identification in a camera network. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 330–345. Springer, Heidelberg (2014)

    Google Scholar 

  13. Hu, Y., Liao, S., Yi, D., et al.: Multi-camera trajectory mining: database and evaluation. In: International Conference on Pattern Recognition (ICPR) (2014)

    Google Scholar 

  14. Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  15. Javed, O., Shafique, K., Rasheed, Z., Shah, M.: Modeling intercamera spacetime and appearance relationships for tracking across non-overlapping views. In: Computer Vision and Image Understand (CVIU) (2008)

    Google Scholar 

  16. Wang, Y., Velipasalar, S., Casares, M.: Cooperative object tracking and composite event detection with wireless embedded smart cameras. IEEE Trans. Image Process. (TIP) 19(10), 2614–2613 (2010)

    Article  MathSciNet  Google Scholar 

  17. Ding, C., Song, B., Morye, A., Farrell, J.A., Roy-Chowdhury, A.K.: Collaborative sensing in a distributed PTZ camera network. IEEE Trans. Image Process. (TIP) 21(7), 3282–3295 (2012)

    Article  MathSciNet  Google Scholar 

  18. Leng, Q., Hu, R., Liang, C., et al.: Bidirectional ranking for person re-identification. In: International Conference on Multimedia and Expo (ICME) (2013)

    Google Scholar 

  19. Li, X., Tao, D., Jin, L., Wang, Y., Yuan, Y.: Person re-identification by regularized smoothing kiss metric learning. IEEE Trans. Circuits Syst. Video Technol. (TCSVT) 23(10), 1675–1685 (2013)

    Article  Google Scholar 

  20. Wang, Y., Hu, R., Liang, C., Zhang, C., Leng, Q.: Camera compensation using a feature projection matrix for person reidentification. IEEE Trans. Circ. Syst. Video Technol. 24(8), 1350–1361 (2014)

    Article  Google Scholar 

  21. Zheng, L., Shen, L., Tian, L., et al.: Person re-identification meets image search. arXiv (2015)

    Google Scholar 

  22. Zheng, W.-S., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 35(3), 653–668 (2013)

    Article  Google Scholar 

  23. Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Computer Vision and Pattern Recognition (CVPR) (2012)

    Google Scholar 

  24. Zhang, S., Staudt, E., Faltemier, T., et al.: A camera network tracking (CamNeT) dataset and performance baseline. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)

    Google Scholar 

  25. Liao, S., Hu, Y., Zhu, X., et al.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  26. Liu, C., Ryen, W., Susan, D.: Understanding web browsing behaviors through Weibull analysis of dwell time. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2010)

    Google Scholar 

Download references

Acknowledgement

The research was supported by National Nature Science Foundation of China (61303114, 61231015, 61170023), the Specialized Research Fund for the Doctoral Program of Higher Education (20130141120024), the Technology Research Project of Ministry of Public Security (2014JSYJA016), the China Postdoctoral Science Foundation funded project (2013M530350), the major Science and Technology Innovation Plan of Hubei Province (2013AAA020), the Guangdong-Hongkong Key Domain Break-through Project of China (2012A090200007), and the Special Project on the Integration of Industry, Education and Research of Guangdong Province (2011B090400601). Nature Science Foundation of Hubei Province (2014CFB712). Jiangxi Youth Science Foundation of China(20151BAB217013).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, China

    Wenxin Huang, Ruimin Hu, Chao Liang & Zheng Wang

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

    Ruimin Hu & Chao Liang

  3. National Institute of Informatics, Tokyo, Japan

    Yi Yu

  4. School of Computer, Wuhan University of Technology, Wuhan, China

    Xian Zhong

  5. University of Chinese Academy of Sciences, Beijing, China

    Chunjie Zhang

Authors
  1. Wenxin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruimin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xian Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chunjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruimin Hu .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Huang, W. et al. (2016). Camera Network Based Person Re-identification by Leveraging Spatial-Temporal Constraint and Multiple Cameras Relations. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27671-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation