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http://dx.doi.org/10.9717/kmms.2014.17.7.787

A vision based people tracking and following for mobile robots using CAMSHIFT and KLT feature tracker  

Lee, S.J. (Chassis&Safety Control Engineering Design Team)
Won, Mooncheol (Dept. of Mechatronics-Eng., Chungnam National Univesity)
Publication Information
Journal of Korea Multimedia Society / v.17, no.7, 2014 , pp. 787-796 More about this Journal
Abstract
Many mobile robot navigation methods utilize laser scanners, ultrasonic sensors, vision camera, and so on for detecting obstacles and path following. However, human utilizes only vision(e.g. eye) information for navigation. In this paper, we study a mobile robot control method based on only the camera vision. The Gaussian Mixture Model and a shadow removal technology are used to divide the foreground and the background from the camera image. The mobile robot uses a combined CAMSHIFT and KLT feature tracker algorithms based on the information of the foreground to follow a person. The algorithm is verified by experiments where a person is tracked and followed by a robot in a hallway.
Keywords
Gaussian Mixture Model; Continuously Adaptive Meanshift; Kanade Lucas Tomasi Tracker; Person Following;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 T. Broida and R. Chellappa,., "Estimation of Object Motion Parameters from Noisy Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 1, pp. 90-99, 1986.
2 D. Comaniciu., V. Rameshi, , And P. Meer , "Kernel-based Object Tracking", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 564-575, 2003.   DOI   ScienceOn
3 John G. Allen, Richard Y.D. Xu, and Jesse S. Jin, "Object Tracking using CamShift Algorithm and Multiple Quantized Feature Spaces," Proceedings of the Pan-Sydney area workshop on Visual information processing. Vol. 36, pp. 3-7, 2003.
4 S. Avidan,, "Support Vector Tracking," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 184-191, 2001.
5 R. Eduardo., I. Fantoni-coichot, A. Sanchez, G. Sanahuja., "Optical Flow-Based Controller for Reactive and Relative and Relative Navigation Dedicated to a Four Rotor Rotorcraft," Proceeding of IEEE International Conference on Intelligent Robots and Systems, pp. 636-689, 2009.
6 H. Bay, E. Andreas, T. Tuytelaars, and L.V. Gool. "Speeded-up Robust Feature," Computer Vision and Image Understanding, Vol. 110, Issue 3, pp. 345-359, 2008.
7 D. Kim, J. Park, and C. Lee "Object-Tracking System Using Combination of CAMshift and Kalman filter Algorithm," Journal of Korea Multimedia Society Vol. 16, No. 5, pp. 619- 628, 2013.   과학기술학회마을   DOI   ScienceOn
8 S.Y. Eum, D.H. Ryu, W.S. Chung, and J.S. Lee, "Moving Object Detection and Automatic Tracking by the Difference Image," The Journal of Institute of Electronics Engineers of Korea, Vol. 87, No. 7, pp. 1387-1389, 1987.
9 C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-time Tracking of the Human Body," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 780-785, 1997.   DOI   ScienceOn
10 C.J. Chang, W.F. Hu, J.W. Hsieh, and Y.S. Chen, "Shadow Elimination for Effective Moving Object Detection with Gaussian Models," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 540-543, 2002.
11 N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 886-893, 2005.
12 Q. Zhu, M.C. Yeh, K.T. Cheng, and S. Avidan, "Fast Human Detection using a Cascade of Histograms of Oriented Gradients," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 1491-1498, 2006.
13 Y. Cui, L. Sun, and S. Yang, "Pedestrian Detection using Improved Histogram of Oriented Gradients," Proceeding of 5th International Conference on Visual Information Engineering, pp. 288-392, 2008.
14 Y. Pang, H. Yan, Y. Yuan, and K. Wang, "Robust CoHOG Feature Extraction in Human-centered Image/Video Management System," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 42, No. 2, pp. 458-568, 2012.   DOI   ScienceOn
15 P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian Detection: An Evaluation of the State of the Art," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 4, pp. 743-761, 2012.   DOI   ScienceOn
16 I. Ulrich and I. Nourbakhsh, "Appearancebased Obstacle Detection with Monocular Color Vision," Proceeding of Association for the Advancement of Artificial Intelligence, pp.1-4, 2000.
17 V. Salari and I.K. Sethi., "Feature Point Correspondence in the Presence of Occlusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, pp. 99- 91, 1990.