{"title":"Object Recognition on Horse Riding Simulator System","authors":"Kyekyung Kim, Sangseung Kang, Suyoung Chi, Jaehong Kim","volume":77,"journal":"International Journal of Mechanical and Materials Engineering","pagesStart":660,"pagesEnd":665,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/7473","abstract":"<p>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.<\/p>\r\n","references":"[1] P. Phillips, \"The FERET Evaluation Methodology for Face Recognition\r\nAlgorithms,\" IEEE Trans. on PAMI. vol.22, pp.1090-1104, 2000.\r\n[2] L. Wiskott, \"Face Recognition by Elastic Bunch Graph Matching,\"\r\nIntelligent Biometric Techniques in Fingerprint and Face Recognition,\r\nCRC Press, ISBN 0-8493-2055-0, Chapter 11, pp.355-396, 1999.\r\n[3] B. Froba and A. Ernst, \"Face Detection with the Modified Census\r\nTransform,\" FGR04, pp.1-6, 2004.\r\n[4] T. Ahonen, A. Hadid, and M. Pietikainen, \"Face Description with Local\r\nBinary Patterns: Application to Face Recognition,\" IEEE Trans. on\r\nPAMI, pp.2037-2041, 2006.\r\n[5] R. Senaratne, S. Halgamuge, and A. Hsu, \"Face Recognition by\r\nExtending Elastic Bunch Graph Matching with Particle Swarm\r\nOptimization,\" Journal of Multimedia, vol.4, no.4, pp.204-214, Aug.,\r\n2009.\r\n[6] R. Ramadan and R. Abdel-kader, \"Face Recognition Using Particle\r\nSwarm Optimization -Based Selected Features,\" International Journal of\r\nSignal Processing, Image Processing and Pattern Recognition, vol.2,\r\nno.2, pp.51-65, June, 2009.\r\n[7] J. H. Kim, \"Fully Automatic Facial Recognition Algorithm By Using\r\nGabor Feature Based Face Graph,\" J. of The Korea Contents Association,\r\nvol.11, no.2, pp.31-39, Feb., 2011.\r\n[8] M. Rao, P. Kumar, V. Kumari, and B. GR, \"Efficient Face Recognition\r\nusing Local Active Pixel Pattern (LAPP) for Mobile Environment,\" CSI\r\nJ. of Computing, vol.1, no.1, pp.5-11, 2012.\r\n[9] S. Belongie, J. Malik and J. Puzicha, \"Shape matching and object\r\nrecognition using shape contexts,\" IEEE Trans. on Pattern Anal. Mach.\r\nIntel., vol. 24, no. 24, pp. 509-522, April, 2004.\r\n[10] C. Lu, N. Adluru, H. Ling, G. Zhu, L. J. Latecki, \u201cContour based object \r\ndetection using part bundle,\u201d Journal of Computer Vision and Image \r\nUnderstanding, vol. 114, Issue 7, pp. 827-834, July, 2010. \r\n[11] V. Ferrari, L. Fevrier, F.Jurie, and C. Schmid, \u201cGroups of adjacent \r\ncontour segments for object detection,\u201d IEEE Trans. on Pattern Anal. \r\nMach. Intel., vol 30, no. 1, pp.36-51, Feb., 2008. \r\n[12] P. F. Felzenszwalb and J. Schwartz, \u201cHierarchical matching of \r\ndeformable shapes,\u201d Computer Vision and Pattern Recognition, pp.1-8, \r\n2007. 6. \r\n[13] D. Lee, and M. S. Nixon, \u201cVision-based finger action recognition by \r\nangle detection and contour analysis,\u201d ETRI Journal, vol. 33, no. 3, pp. \r\n415-422, June, 2011. \r\n[14] V. Ferrari, F. Jurie, and C. Schmid, \u201cAccurate object detection with \r\ndeformable shape models learnt from images,\u201d Computer Vision and \r\nPattern Recognition, pp.1-8, June, 2007. \r\n[15] P. Felzenszwalb, \u201cRepresentation and detection of deformable shapes,\u201d \r\nPAMI, vol. 27, no. 2, pp. 208~220, Feb., 2005. \r\n[16] D. Martin, C. Fowlkes, and J. Malik, \u201cLearning to detect natural image \r\nboundaries using local brightness, color, and texture cues,\u201d PAMI, vol. \r\n26, no. 5, pp. 530~549, May, 2004. \r\n[17] N. Ueda and S. Suzuki, \u201cLearning visual models from shape contours \r\nusing multiscale convex\/concave structure matching,\u201d PAMI, vol. 15, no. \r\n4, pp. 337~352, 1993.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 77, 2013"}