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http://dx.doi.org/10.9717/kmms.2016.19.10.1767

Correction Method of Movement Path for Depth Touch by Adaptive Filter  

Lee, Dong-Seok (Dept. of Computer Software Engineering, Dongeui University)
Kwon, Soon-Kak (Dept. of Computer Software Engineering, Dongeui University)
Publication Information
Abstract
In this paper, we propose the adaptation filtering for correcting the movement path of the recognized object by the depth information. When we recognize the object by the depth information, the path error should be occurred because of the noises in the depth information. The path error is corrected by appling the lowpass filtering, but the lowpass filtering is not efficient when the changes of the object's movement are rapid. In this paper, we apply the adaptation filtering that it gives weights adaptively as the difference between the predicted location and the measured location. To apply the adaptation filtering, we can see that the proposed method can correct accurately the path error of the radical change from simulation results.
Keywords
Depth Measure Sensor; Depth Information; Error Correction; Adaptation Filtering;
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1 A. Mohan, C. Papageorgiou, and T. Poggio, “Example-Based Object Detection in Images by Components,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 4, pp. 349-361, 2001.   DOI
2 H. Tao, H.S. Sawhney, and R. Kumar, “Object Tracking with Bayesian Estimation of Dynamic Layer Representations,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 75-89, 2002.   DOI
3 D. Comaniciu and P. Meer, “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Proceeding of IEEE Conference Computer Vision and Pattern Recognition, Vol. 20, pp. 142-149, 2000.
4 L. Li and M.K.H. Leung, “Integrating Intensity and Texture Differences for Robust Change Detection,” IEEE Transactions on Image Processing, Vol. 11, No. 2, pp. 105-112, 2002.   DOI
5 M. Heikkila and M. Pietikainen, “A Texture based Method for Modeling the Background and Detecting Moving Objects,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 657-662, 2006.   DOI
6 L. Huang, G. Zhang, and Y. Li, “An Object-based Change Detection Approach by Integrating Intensity and Texture Differences,” Proceeding of 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Vol. 3, pp. 258-261, 2010.
7 E. Land and J. McCann, “Lightness and Retinex Theory,” Journal of the Optical Society of America A, Vol. 61. No. 1, pp. 1-11, 1971.   DOI
8 J. Chung and H. Yang. “Comparative Study on Illumination Compensation Performance of Retinex Model and Illumination-Reflectance Model,” Journal of KISSE : Software and Applications, Vol. 33, No. 11, pp. 936-941, 2006.
9 K. Lai, L. Bo, X. Ren, and D. Fox, “Sparse Distance Learning for Object Recognition Combining RGB and Depth Information,” Proceeding of 2011 IEEE International Conference on Robotics and Automation, pp. 4007-4013, 2011.
10 H. Min, S. Noh, and Y. 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.   DOI
11 B. Choo, M. Landau, M. DeVore, and P.A. Beling, “Statistical Analysis-Based Error Models for the Microsoft KinectTM Depth Sensor,” Sensors, Vol. 14, No. 9, pp. 17430-17450, 2014.   DOI
12 H.A. Patel and D.G. Thakore, “Moving Object Tracking Using Kalman Filter,” International Journal of Computer Science and Mobile Computing, Vol. 2, No. 4, pp. 326-332, 2013.
13 C.V. Nguyen, S. Izadi, and D. Lovell, “Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking,” Processing of 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 524-530, 2012.
14 Y. Zhang, J. Li, and N. Xu, “3D Path Following Control for UAVs Using L1 Adaptive Method,” Proceeding of Chinese Automation Congress, pp. 1098-1104, 2015.
15 R. Ren, Z. Zou, and X. Wang, “L1 Adaptive Control Used in Path Following of Surface Ships,” Proceeding of 2014 33rd Chinese Control Conference, pp. 8047-8053, 2014.
16 G. Du and P. Zhang, “A Markerless Human-Robot Interface Using Particle Filter and Kalman Filter for Dual Robots,” IEEE Transactions on Industrial Electronics, Vol. 62, No. 4, pp. 2257-2264, 2015.   DOI
17 D. Lee and S. Kwon, “Touch Pen Using Depth Information,” Journal of Korea Multimedia Society, Vol. 18, No. 11, pp. 1313-1318, 2015.   DOI