Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Efficient Object Tracking System Using the Fusion of a CCD Camera and an Infrared Camera

CCD카메라와 적외선 카메라의 융합을 통한 효과적인 객체 추적 시스템

  • 김승훈 (전자부품연구원, 지능로보틱스연구센터) ;
  • 정일균 (전자부품연구원, 지능로보틱스연구센터) ;
  • 박창우 (전자부품연구원, 지능로보틱스연구센터) ;
  • 황정훈 (전자부품연구원, 지능로보틱스연구센터)
  • Received : 2010.11.15
  • Accepted : 2010.12.20
  • Published : 2011.03.01
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Abstract

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.

Keywords

References

  1. A. Al-Habaibeh, F. Shi, N. Brown, D. Kerr, M. Jackson, and R. M. Parkin, "A novel approach for quality control system using sensor fusion of infrared and visual image processing for laser sealing of food containers," Measurement Science and Technology, vol. 15, pp. 1995-2000, 2004. https://doi.org/10.1088/0957-0233/15/10/008
  2. P. Simard, N. K. Link, and R. V. Kruk, "Evaluation of algorithms for fusing infrared and synthetic imagery," Enhanced and synthetic vision 2000, Conference, Orlando FL, vol. 4023, pp. 127-138, 2000.
  3. A. Malviya and S. G. Bhirud, "Visual infrared video fusion for night vision using background estimation," Journal of Computing, vol. 2, no. 4, Apr. 2010.
  4. L. Leyrit, T. Chateau, C Tournayre, and J. Lapreste, "Association of AdaBoost and kernel-based machine learning methods for visual pedestrian recognition," IEEE Intelligent Vehicles Symposium, pp. 67-72, 2008. https://doi.org/10.1109/IVS.2008.4621294
  5. K. Ohyun, K. Sangjin, J. Junghoon, S. Jeongho, and P. Joonki, "Auto calibration for multiple camera-based surveillance system," 제17회 신호처리 합동학술대회, 2004.
  6. J. Shin, S. Jang, S. Kim, C. Park, and J. Paik, "Optical flow-based, real-time object tracking using the non-prior training active feature model," Real-time Imaging, vol. 11, no. 3, pp. 204-218, 2005. https://doi.org/10.1016/j.rti.2005.03.006
  7. C. Wren, A. Azerbayejani, T. Darrel, and A. Pentland, "Pfinder: real-time tracking of the human body," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, 1997. https://doi.org/10.1109/34.598236
  8. S. Kang, J. Paik, A. Koschan, B. Abidi, and A. Abidi, "Real-time video tracking using PTZ cameras," Proc. SPIE 6th International Conference on Quality Control by Artificial Vision, vol. 5132, pp. 103-111, 2003.
  9. P. Azzari, L. Stefano, and A. Bevilacqua, "An effective real-time mosaicing algorithm apt to detect motion through background subtraction using a PTZ camera," IEEE Conf. Advanced Video and Signal-Based Surveillance, pp. 511-516, 2005. https://doi.org/10.1109/AVSS.2005.1577321
  10. R. Lienhart and J. Maydt, "An extended set of HAAR-like features for rapid object detection," International Conference on Image Processing, vol. 1, pp, 900-903, 2002.
  11. G. A. Ramirez and O. Fuentes, "Multi-pose face detection with asymmetric HAAR features," Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop, pp. 1-6, 2008. https://doi.org/10.1109/WACV.2008.4544013
  12. J. Sewoong, L. Jongbae, and K. Youngouk, "Illumination invariant head pose recognition using thermal camera," Proc. of the 39th International Symposium on Robotics, pp. 15-17, 2008.
  13. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," IEEE Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886-893, 2008. https://doi.org/10.1109/CVPR.2005.177