Real-Time Infrared Object Tracking Based on Mean Shift

  • Cheng Jian
  • Yang Jie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


Themean shift algorithm is an efficient method for tracking object in the color image sequence. However, in the infrared object-tracking scenario, there is a singular feature space, i.e. the grey space, for representing the infrared object. Due to the lack of the information for the object representation, the object tracking based on the mean shift algorithm may be lost in the infrared sequence. To overcome this disadvantage, we propose a new scheme that is to construct a cascade grey space. The experimental results performed on two different infrared image sequences show our new scheme is efficient and robust for the infrared object tracking.


  1. 1.
    Fukanaga, K., Hostetler, L.D.: The Estimation of the Gradient of a Density Function, with Application in Pattern Recognition. IEEE Trans. Information Theory 21, 32–40 (1975)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)CrossRefGoogle Scholar
  3. 3.
    Bradski, G.R.: Real Time Face and Object Tracking as a Component of a Perceptual User Interface. In: Proc. IEEE Workshop on Applications of Computer Vision, Princeton, pp. 214–219 (1998)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach towards Feature Space Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 142–149 (2000)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)CrossRefGoogle Scholar
  7. 7.
    Shekarforoush, H., Chellappa, R.: A Multi-Fractal Formalism for Stabilization, Object Detection and Tracking in FLIR Sequence. In: IEEE International Conference on Image Processing, vol. 3 (2000)Google Scholar
  8. 8.
    Daviesy, D., Palmery, P., Mirmehdiz, M.: Detection and Tracking of Very Small Low Contrast Objects. In: Ninth British Machine Vision Conference (1998)Google Scholar
  9. 9.
    Webb, A.R.: Statistical Pattern Recognition, 2nd edn. Wiley, Chichester (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Cheng Jian
    • 1
  • Yang Jie
    • 1
  1. 1.Institute of Image Processing & Pattern RecognitionShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations