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Real-time follow-up head tracking in dynamic complex environments

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Abstract

In the modes of both object motion and camera motion, an enhanced Camshift algorithm, which is based on suppressing similar color features of background and on joint color probability density distribution image, is proposed to real-time track head in dynamic complex environment. The system consists of face detection module, head tracking module and camera control module. When tracking fails, a self-recovery mechanism is introduced. At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces, the false positive is filtered according to the skin color criterion, and the true face is used to initialize the tracking module. In hue saturation value (HSV) colorspace, the hue-saturation (H-S) histogram of face skin and the saturation-value (S-V) histogram of hair are built to produce the joint color probability density distribution image, and this is intended to realize the head tracking with arbitrary pose. During tracking, region of interest (ROI) is introduced, and the color probability density distribution of a specified background area outside the ROI is learned, similar color features in the head are suppressed according to the learning result. The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head. A closed loop control model based on speed regulation is applied to drive an active camera to center the head. Once tracking drift or failure is detected, the system stops tracking and returns to the face detection module. Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments, also the active camera can track moving head smoothly and stably. The system is computationally efficient and can run in real-time completely.

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References

  1. Nait-charif H, Mckenna S J. Head tracking and action recognition in a smart meeting room [C]// The IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. Graz, Austria: IEEE, 2003: 24–31.

    Google Scholar 

  2. Zhang J Z, Ye L, Wu Q M. Real time head tracking via camera saccade and shape-fitting [C]// The Second International Conference on Image Analysis and Recognition. Heidelberg, Berlin: Springer-Verlag, 2005: 828–835.

    Google Scholar 

  3. Zhang C, Rui Y, He L, et al. Hybrid speaker tracking in an automated lecture room [C]// IEEE International Conference on Multimedia and Expo. [s.l.]: IEEE, 2005: 81–84.

    Chapter  Google Scholar 

  4. Schwerdt K, Crowley L J. Robust face tracking using color [C]// In Proceedings of the Fourth IEEE International conference on Automatic Face and Gesture Recognition. Grenoble, France: IEEE, 2000: 90–95.

    Chapter  Google Scholar 

  5. Comaniciu D, Ramesh V. Robust detection and tracking of human faces with an active camera [C]// In Proceedings of IEEE International Workshop on Visual Surveillance. Dublin, Ireland: IEEE, 2000: 11–18.

    Chapter  Google Scholar 

  6. Bradski G R. Real-time face and object tracking as a component of a perceptual user interface [C]// Proceeding of IEEE Workshop on Application of Computer Vision. Washington, D C, USA: IEEE, 1998: 214–219.

    Google Scholar 

  7. Birchfield S T. Elliptical head tracking using intensity gradients and color histograms [C]// In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC, USA: IEEE, 1998: 232–237.

    Google Scholar 

  8. Jeong D, Yang Y K, Kang D G, et al. Real-time head tracking based on color and shape information [C]// Proceeding of Image and Video Communications. Bellingham, WA: SPIE, 2005: 912–923.

    Google Scholar 

  9. Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–577.

    Article  Google Scholar 

  10. Viola P, Jones M. Robust real-time face detection [J]. International Journal of Computer Vision, 2004, 57(2): 137–154.

    Article  Google Scholar 

  11. Sung K K, Poggio T. Example-based learning for view-based human face detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(1): 39–51.

    Article  Google Scholar 

  12. Swain M J, Ballard D H. Color indexing [J]. International Journal of Computer Vision, 1991, 7(1): 11–32.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of Mechatronic Control Engineering, Zhejiang University, Hangzhou, 310027, China

    Gui-shan Xiang  (向桂山) & Xuan-yin Wang  (王宣银)

Authors
  1. Gui-shan Xiang  (向桂山)
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  2. Xuan-yin Wang  (王宣银)
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Correspondence to Gui-shan Xiang  (向桂山).

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Xiang, Gs., Wang, Xy. Real-time follow-up head tracking in dynamic complex environments. J. Shanghai Jiaotong Univ. (Sci.) 14, 593–599 (2009). https://doi.org/10.1007/s12204-009-0593-2

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  • DOI: https://doi.org/10.1007/s12204-009-0593-2

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