Object Recognition on Horse Riding Simulator System

Authors: Kyekyung Kim, Sangseung Kang, Suyoung Chi, Jaehong Kim

Abstract:

In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. In this paper, object detection and recognition using vision sensor applied to sports simulator has been introduced. Face recognition has been processed to identify user and to update automatically a person athletic recording. Human body has tracked to offer a most accurate way of riding horse simulator. Combined image processing has been processed to reduce illumination adverse affect because illumination has caused low performance in detection and recognition in real world application filed. Face has recognized using standard face graph and human body has tracked using pose model, which has composed of feature nodes generated diverse face and pose images. Face recognition using Gabor wavelet and pose recognition using pose graph is robust to real application. We have simulated using ETRI database, which has constructed on horse riding simulator.

Keywords: Horse riding simulator, Object detection, Object recognition, User identification, Pose recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333871

References:

[1] P. Phillips, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Trans. on PAMI. vol.22, pp.1090-1104, 2000.
[2] L. Wiskott, "Face Recognition by Elastic Bunch Graph Matching," Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC Press, ISBN 0-8493-2055-0, Chapter 11, pp.355-396, 1999.
[3] B. Froba and A. Ernst, "Face Detection with the Modified Census Transform," FGR04, pp.1-6, 2004.
[4] T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. on PAMI, pp.2037-2041, 2006.
[5] R. Senaratne, S. Halgamuge, and A. Hsu, "Face Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization," Journal of Multimedia, vol.4, no.4, pp.204-214, Aug., 2009.
[6] R. Ramadan and R. Abdel-kader, "Face Recognition Using Particle Swarm Optimization -Based Selected Features," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol.2, no.2, pp.51-65, June, 2009.
[7] J. H. Kim, "Fully Automatic Facial Recognition Algorithm By Using Gabor Feature Based Face Graph," J. of The Korea Contents Association, vol.11, no.2, pp.31-39, Feb., 2011.
[8] M. Rao, P. Kumar, V. Kumari, and B. GR, "Efficient Face Recognition using Local Active Pixel Pattern (LAPP) for Mobile Environment," CSI J. of Computing, vol.1, no.1, pp.5-11, 2012.
[9] S. Belongie, J. Malik and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. on Pattern Anal. Mach. Intel., vol. 24, no. 24, pp. 509-522, April, 2004.
[10] C. Lu, N. Adluru, H. Ling, G. Zhu, L. J. Latecki, “Contour based object detection using part bundle,” Journal of Computer Vision and Image Understanding, vol. 114, Issue 7, pp. 827-834, July, 2010.
[11] V. Ferrari, L. Fevrier, F.Jurie, and C. Schmid, “Groups of adjacent contour segments for object detection,” IEEE Trans. on Pattern Anal. Mach. Intel., vol 30, no. 1, pp.36-51, Feb., 2008.
[12] P. F. Felzenszwalb and J. Schwartz, “Hierarchical matching of deformable shapes,” Computer Vision and Pattern Recognition, pp.1-8, 2007. 6.
[13] D. Lee, and M. S. Nixon, “Vision-based finger action recognition by angle detection and contour analysis,” ETRI Journal, vol. 33, no. 3, pp. 415-422, June, 2011.
[14] V. Ferrari, F. Jurie, and C. Schmid, “Accurate object detection with deformable shape models learnt from images,” Computer Vision and Pattern Recognition, pp.1-8, June, 2007.
[15] P. Felzenszwalb, “Representation and detection of deformable shapes,” PAMI, vol. 27, no. 2, pp. 208~220, Feb., 2005.
[16] D. Martin, C. Fowlkes, and J. Malik, “Learning to detect natural image boundaries using local brightness, color, and texture cues,” PAMI, vol. 26, no. 5, pp. 530~549, May, 2004.
[17] N. Ueda and S. Suzuki, “Learning visual models from shape contours using multiscale convex/concave structure matching,” PAMI, vol. 15, no. 4, pp. 337~352, 1993.