Abstract
This study proposes a continuous gesture recognition using information about features of motions obtained from continuous gesture images through higher order correlation feature coefficient and principal component analysis (PCA). The proposed method, first, separates two-dimensional silhouette gesture region from a continuous input image that includes a human body image. Information about 35 features are extracted using the higher order local auto correlation coefficient in the divided image and a low-dimensional gesture space is composed using PCA. The model feature value reflected in the gesture space is composed of symbols of certain conditions through clustering algorithm so as to be used as the input symbol of a hidden marker model, and a random input motion is recognized as the relevant gesture model with the highest probability value. The proposed method has less computation workload than the previous geometric feature-based method or appearance-based method and shows a high recognition rate with using the minimum information, so it is very suitable for the construction of a real-time system. In addition, the recognized gesture can be used input data of various applications like dynamic motion type game by mapping to an operating system. And the human motions can be used the objects of observation, such as smart home and behavior analysis in special spaces.
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References
Weiser, M. (2001). The computer for the 21st century. Scientific America, 265(3), 66–76.
Pavlovic, V. I., Sharma, R., & Huang, T. S. (1997). Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 677–695.
Turk, M. (2004). Computer vision in the interface. Communications of the ACM, 47(1), 60–67.
Gavrila, D. M., Davis, L. S. (1995). Towards 3D model based tracking and recognition of human movement: A multi-view approach. In Proceedings of the international workshop on face and gesture recognition (pp. 272–277).
Haritaoglu, I., Harwood, D., Davis, L. S. (1998). W4: Who? When? Where? What? A real-time system for detecting and tracking people. In Proceedings of the international conference on face and gesture recognition (pp. 222–227).
Haritaoglu, I., Harwood, D., Davis, L. S. (1998). Ghost: A human body part labeling system using silhouettes. In Proceedings of the international conference on pattern recognition (p. 77).
Sherrah, J., Gong, S. (2000). VIGOUR: A system for tracking and recognition of multiple people and their activities. In Proceedings of the international conference on pattern recognition (p. 1179).
Watanabe, T., Yachida, M. (1998). Real time recognition of gesture and gesture degree information using multi input image sequence. In Proceedings of the international conference on pattern recognition (p. 1855).
Davis, J. W., Bobic, A. F. (1997). The representation and recognition of action using temporal templates. MIT Media lab technical report 402.
Ishii, I., & Kubozono, M. (2006). Higher order autocorrelation vision chip. IEEE Transactions on Electron Devices, 53(8), 1797–1804.
Murase, H., & Nayar, S. K. (1995). Visual learning and recognition 3-D object from appearance. International Journal of Computer Vision, 14(1), 5–24.
Caelli, T., McCane, B. (2013). Components analysis of hidden Markov models in computer vision. In Proceedings of international conference image analysis and processing (pp. 510–515).
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.
Kang, S. K., Chung, K. Y., & Lee, J. H. (2014). Real-time tracking and recognition systems for interactive telemedicine health services. Wireless Personal Communications, 79(4), 2611–2626.
Kang, S. K., Chung, K. Y., & Lee, J. H. (2015). Ontology based inference system for adaptive object recognition. Multimedia Tools and Applications. doi:10.1007/s11042-013-1738-8.
Kang, S. K., Chung, K. Y., & Lee, J. H. (2014). development of head detection and tracking systems for visual surveillance. Personal and Ubiquitous Computing, 18(3), 515–522.
Kim, J. C., Jung, H., Kim, S. H., & Chung, K. (2015). Slope based intelligent 3D disaster simulation using physics engine. Wireless Personal Communications. doi:10.1007/s11277-015-2788-1.
Jung, H., & Chung, K. Y. (2014). Mining based associative image filtering using harmonic mean. Cluster Computing, 17(3), 767–774.
Chung, K. Y., & Lee, J. H. (2004). User preference mining through hybrid collaborative filtering and content-based filtering in recommendation system. IEICE Transaction on Information and Systems, E87-D(12), 2781–2790.
Chung, K., Boutaba, R., & Hariri, S. (2014). Recent trends in digital convergence information system. Wireless Personal Communications, 79(4), 2409–2413.
Kim, S. H., & Chung, K. (2015). Emergency situation monitoring service using context motion tracking of chronic disease patients. Cluster Computing, 18(2), 747–759.
Jung, H., & Chung, K. (2015). Knowledge based dietary nutrition recommendation for obesity management. Information Technology and Management. doi:10.1007/s10799-015-0218-4.
Jung, H., & Chung, K. (2015). Sequential pattern profiling based bio-detection for smart health service. Cluster Computing, 18(1), 209–219.
Jung, H., & Chung, K. Y. (2014). Discovery of automotive design paradigm using relevance feedback. Personal and Ubiquitous Computing, 18(6), 1363–1372.
Kim, J. H., & Ryu, J. K. (2013). Recent trends on high-performance computing and security. Cluster Computing, 16(2), 207–208.
Oh, S. Y., Ghose, S., Chung, K., & Han, J. S. (2014). Recent trends in convergence based smart healthcare service. International Journal of Technology and Health Care, 22(3), 303–307.
Chung, K., Kim, J. C., & Park, R. C. (2015). Knowledge based health service considering user convenience using hybrid Wi-Fi P2P. Information Technology and Management. doi:10.1007/s10799-015-0241-5.
Jung, H., Yoo, H., Lee, Y. H., & Chung, K. Y. (2015). Interactive pain nursing intervention system for smart health service. Multimedia Tools and Applications, 74(7), 2449–2466.
Kim, J. H., & Chung, K. Y. (2014). Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications, 71(2), 873–888.
Kim, S. H., & Chung, K. Y. (2014). 3D simulator for stability analysis of finite slope causing plane activity. Multimedia Tools and Applications, 68(2), 455–463.
Oh, S. Y., Ghose, S., Jeong, Y. K., Ryu, J. K., & Han, J. (2015). Convergence security systems. International Journal of Computer Virology and Hacking, 11(3), 119–121.
Han, J. (2015). Distributed hybrid P2P networking systems. Peer-to-Peer Networking and Applications, 8(4), 555–556.
Kim, J., & Lee, J. (2015). Mobile, ubiquitous multimedia and digital convergence. Cluster Computing, 18(1), 243–245.
Chung, K., & Oh, S. Y. (2015). Improvement of speech signal extraction method using detection filter of energy spectrum entropy. Cluster Computing, 18(2), 629–635.
Davis, J. W. (1996). Appearance-based motion recognition of human actions. MIT Media Lab perceptual computing group technical report, No. 387.
Ahad, M. A. R., Tan, J. K., Kim, H., & Ishikawa, S. (2012). Motion history image: Its variants and applications. Machine Vision and Applications, 23(2), 255–281.
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This Study was conducted by research funds from Gwangju University in 2015.
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Kim, J., Chung, K. & Kang, M. Continuous Gesture Recognition using HLAC and Low-Dimensional Space. Wireless Pers Commun 86, 255–270 (2016). https://doi.org/10.1007/s11277-015-3068-9
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DOI: https://doi.org/10.1007/s11277-015-3068-9