JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Tracking Method for Moving Object Using Depth Picture
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Tracking Method for Moving Object Using Depth Picture
Kwon, Soon-Kak; Kim, Heung-Jun;
  PDF(new window)
 Abstract
The conventional methods using color signal for tracking the movement of the object require a lot of calculation and the performance is not accurate. In this paper, we propose a method to effectively track the moving objects using the depth information from a depth camera. First, it separates the background and the objects based on the depth difference in the depth of the screen. When an object is moved, the depth value of the object becomes blurred because of the phenomenon of Motion Blur. In order to solve the Motion Blur, we observe the changes in the characteristics of the object (the area of the object, the border length, the roundness, the actual size) by its velocity. The proposed algorithm was implemented in the simulation that was applied directly to the tracking of a golf ball. We can see that the estimated value of the proposed method is accurate enough to be very close to the actual measurement.
 Keywords
Depth Information;Object Extraction;Motion Estimation;Motion Blur;
 Language
Korean
 Cited by
1.
야외 RGB+D 데이터베이스 구축을 위한 깊이 영상 신뢰도 측정 기법,박재광;김선옥;손광훈;민동보;

한국멀티미디어학회논문지, 2016. vol.19. 9, pp.1647-1658 crossref(new window)
 References
1.
J.L. Barron, D.J. Fleet, and S.S. Beauchemin, “Systems and Experiment: Performance of Optical Flow Techniques,” International Journal of Computer Vision, Vol. 12, No. 1, pp. 43-77, 1994. crossref(new window)

2.
Y.Q. Shi and X.Xia, “A Thresholding Multiresolution Block Matching Algorithm”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 7, No. 2, pp. 437-440, 1997. crossref(new window)

3.
H.B. Kim, K.E. Ko, J.S. Kang, and K.B. Sim, “Specified Object Tracking in an Environment of Multiple Moving Objects Using Particle Filter,” Journal of Korean Institute of Intelligent Systems, Vol. 21, No. 1, pp. 106-111, 2011. crossref(new window)

4.
K.C. Kim, “Optimal Structures of a Neural Network Based on OpenCV for a Golf Ball Recognition,” Journal of Korea Institute of Electronic Communication Sciences, Vol. 10, No. 2, pp. 267-273, 2015. crossref(new window)

5.
S.K. Kwon and S.W. Kim, “Motion Estimation Method by Using Depth Camera,” Journal of Korean Society of Broadcast Engineers, Vol. 17, No. 4, pp. 676-683, 2012.

6.
S.K. Kwon, Y.H. Park, and K.R. Kwon, “Zoom Motion Estimation Method by Using Depth Information,” Journal of Korea Multimedia Society, Vol. 16, No. 2, pp. 131-137, 2013. crossref(new window)

7.
H.H. Min, S.H. Noh, and Y.T. Kim, “Moving Object Tracking System Using Location Information Based on Stereo Images,” Journal of Korean Institute of Intelligent Systems, Vol. 20, No. 2, pp. 239-240, 2010. crossref(new window)

8.
K. Lai, L. Bo, X. Ren, and D. Fox, "Sparse Distance Learning for Object Recognition Combining RGB and Depth Information," Proceeding of IEEE International Conference on Robotics and Automation, pp. 4007-4013, 2011.

9.
K. Nam and Y. Shin, “A Design of Over-driving Controller to Reduce Motion Blur,” Journal of Institute of Electronics Engineers of Korea, Vol. 47, No. 4, pp. 1-6, 2010.

10.
S.K. Kwon and D.S. Lee, “Correction of Perspective Distortion Image Using Depth Information,” Journal of Korea Multimedia Society, Vol. 18, No. 2, pp. 106-112, 2015. crossref(new window)