Soft-Biometrics and Reference Set Integrated Model for Tracking Across Cameras



Multi-target tracking in non-overlapping cameras is challenging due to the vast appearance change of the targets across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Therefore, direct track association based on color information only is difficult and prone to error. In most previous methods the appearance similarity is computed either using color histograms directly or based on pre-trained Brightness Transfer Function (BTF) that maps color between cameras. In this chapter, besides color histograms, other soft-biometric features that are invariant to illumination and view changes are also integrated into the feature representation of a target. A novel reference set based appearance model is proposed to improve multi-target tracking in a network of non-overlapping video cameras. Unlike previous work, a reference set is constructed for a pair of cameras, containing targets appearing in both camera views. For track association, instead of comparing the appearance of two targets in different camera views directly, they are compared to the reference set. The reference set acts as a basis to represent a target by measuring the similarity between the target and each of the individuals in the reference set. The effectiveness of the proposed method over the baseline models on challenging real-world multi-camera video data is validated by the experiments.


Soft-biometric Features Brightness Transfer Function (BTF) Track Association Multi-target Tracking Appearance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by NSF grant 0905671 and ONR grant N00014-12-1-1026. The authors would like to acknowledge the soft-biometrics data provided by Progeny that have been used in this work. The contents and information do not reflect the position or policy of the U.S. Government.


  1. 1.
    Abdi H (2007) Kendall rank correlation. SAGE Publications, Inc., California, pp 509–511Google Scholar
  2. 2.
    An L, Chen X, Kafai M, Yang S, Bhanu B (2013) Improving person re-identification by soft biometrics based reranking. In: ACM/IEEE international conference on distributed smart cameras (ICDSC), pp 1–6Google Scholar
  3. 3.
    An L, Kafai M, Yang S, Bhanu B (2013) Reference-based person re-identification. In: IEEE international conference on advanced video and signal-based surveillance (AVSS)Google Scholar
  4. 4.
    Candamo J, Shreve M, Goldgof D, Sapper D, Kasturi R (2010) Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans Intell Transp Syst 11(1): 206–224Google Scholar
  5. 5.
    Chu CT, Hwang JN, Lan KM, Wang SZ (2011) Tracking across multiple cameras with overlapping views based on brightness and tangent transfer functions. In: Fifth ACM/IEEE international conference on distributed smart cameras (ICDSC), pp 1–6Google Scholar
  6. 6.
    Chu CT, Hwang JN, Yu JY, Lee KZ (2012) Tracking across nonoverlapping cameras based on the unsupervised learning of camera link models. In: Sixth International conference on distributed smart cameras (ICDSC), pp 1–6Google Scholar
  7. 7.
    Cover TM, Thomas JA (1991) Elements of information theory. Wiley-Interscience, New YorkCrossRefzbMATHGoogle Scholar
  8. 8.
    Dantcheva A, Velardo C, D’angelo A, Dugelay JL (2011) Bag of soft biometrics for person identification. Multimedia Tools Appl 51(2):739–777CrossRefGoogle Scholar
  9. 9.
    D’Orazio T, Mazzeo P, Spagnolo P (2009) Color brightness transfer function evaluation for non overlapping multi camera tracking. In: Third ACM/IEEE international conference on distributed smart cameras (ICDSC), pp 1–6Google Scholar
  10. 10.
    Gilbert A, Bowden R (2006) Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In: European conference on computer vision 3952:125–136Google Scholar
  11. 11.
    Guo G, Mu G, Ricanek K (2010) Cross-age face recognition on a very large database: the performance versus age intervals and improvement using soft biometric traits. In: 20th International conference on pattern recognition (ICPR), pp 3392–3395Google Scholar
  12. 12.
    Hu W, Hu M, Zhou X, Tan T, Lou J, Maybank S (2006) Principal axis-based correspondence between multiple cameras for people tracking. IEEE Trans Pattern Anal Mach Intell 28(4): 663–671Google Scholar
  13. 13.
    Jain AK, Park U (2009) Facial marks: soft biometric for face recognition. In: ICIPGoogle Scholar
  14. 14.
    Javed O, Shafique K, Rasheed Z, Shah M (2008) Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Comput Vision Image Underst 109:146–162CrossRefGoogle Scholar
  15. 15.
    Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1)Google Scholar
  16. 16.
    Prosser B, Gong S, Xiang T (2008) Multi-camera matching using bi-directional cumulative brightness transfer functions. In: Proceedings of the British machine vision conferenceGoogle Scholar
  17. 17.
    Qin Z, Shelton CR (2012) Improving multi-target tracking via social grouping. In: IEEE conference on computer vision and pattern recognitionGoogle Scholar
  18. 18.
    Reid D, Samangooei S, Chen C, Nixon M, Ross A (2013) Soft biometrics for surveillance: an overview. In: Machine learning: theory and applications. Elsevier, pp 327–352Google Scholar
  19. 19.
    Reid DA, Nixon M (2011) Using comparative human descriptions for soft biometrics. In: International joint conference on biometrics (IJCB), pp 1–6Google Scholar
  20. 20.
    Saleemi I, Shafique K, Shah M (2009) Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Trans Pattern Anal Mach Intell 31(8):1472–1485Google Scholar
  21. 21.
    Schroff F, Treibitz T, Kriegman D, Belongie S (2011) Pose, illumination and expression invariant pairwise face-similarity measure via doppelgänger list comparison. In: IEEE international conference on computer vision (ICCV), pp 2494–2501Google Scholar
  22. 22.
    Siebel NT, Maybank SJ (2004) The advisor visual surveillance system. In: ECCV 2004 workshop applications of computer vision (ACV)Google Scholar
  23. 23.
    Wang X (2013) Intelligent multi-camera video surveillance: a review. Pattern recognition letters 34(1):3–19Google Scholar
  24. 24.
    Yin Q, Tang X, Sun J (2011) An associate-predict model for face recognition. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR), pp 497–504Google Scholar
  25. 25.
    Zhu Y, Nayak N, Roy-Chowdhury A (2013) Context-aware activity recognition and anomaly detection in video. IEEE J Sel Top Sign Proces 7(1):91–101CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of CaliforniaRiversideUSA

Personalised recommendations