本論文將手勢辨識發展於日常生活上，改善遙控式與接觸式功能產品較難以實現的情況。DSP(Digital Signal Process)系統在訊號的處理上具有很好的效能，其可透過內部的硬體架構來達到平行處理的運算，並由於其低耗電量的特色使個人化與攜帶式的資訊產品為運用此硬體的關鍵之一。故本論文在DSP嵌入式系統開發板上開發出一套系統，在電器上結合手部追蹤與手勢辨識，讓使用者僅需透過自己的雙手便能以遠距離使用產品的功能，提供了更輕鬆，更便利的生活。在本論文中，我們提出掃描法、累加值運算法與比例法進行手部的定位，在追蹤上混合了Harr-like feature與The Orientated FAST and Rotated BRIEF(ORB)兩個方法來完成。由實驗的結果可以得知，利用我們提出的方法進行手部追蹤與辨識，不但可以在受干擾的環境下有效的完成指令，亦可達到即時手勢辨識的效果。
關鍵字: 手勢辨識、嵌入式系統、DSP、Harr-like feature、ORB。

This article will let gesture recognition developed in daily life, and improved remote control and touch-function’s situation which is difficult to achieve.
DSP (Digital Signal Process) in signal processing has a good performance, which may be achieve parallel processing operations through internal hardware architecture, there are many manufacturers use DSP image processing or identification features, and one of the key to use this hardware is because of its low power consumption characteristics of the personal and portable information product. So in this paper, we used DSP embedded system board develop a set of systems, which is applied combined with hands tracking on television and gesture recognition, allowing users to control the functions only by their own hands in long distance, which providing easier and more convenient life.
In this paper, we propose a scanning method, the accumulated value calculation method and proportion method for hand positioning, re-use Harr-like feature, and The Orientated FAST and Rotated BRIEF looking for hand features to complete hand tracking and recognition.
From the experimental results, the use of the proposed method for hand tracking and recognition, which is not only can achieve real time hand tracking results but also can also effectively complete instructions under the disturbed environments.
Key word: Gesture Recognition、Embedded System、DSP、Harr-like feature、ORB。

[1] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp.971-987, 2002.
[2] Omachi, S. “Fast Template Matching With Polynomials”, Image Processing, IEEE Transactions , vol. 16, pp. 2139-2149, 2007.
[3] M.S. Kakumanu P., Bourbakis N., “A survey of skin-colour modeling and detection methods”, Pattern Recognition 2007, pp. 1106 – 1122.
[4] Ken Chen, Songyin Fu, Kangkang Song, “A Meanshift-based Imbedded Computer Vision System Design for Real-time Target Tracking”, Computer Science & Education (ICCSE), 2012 7th International of IEEE, pp. 1298-1303, 2012.
[5] Asaari, M.S.M., Suandi, S.A., “Hand Gesture Tracking System Using Adaptive Kalman FilterAdaptive Kalman Filter”, Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, pp. 166-171, Dec 2010.
[6] Yang Xiao, “Entropic Thresholding Based on Gray-level Spatial Correlation Histogram”, Pattern Recognition, 2008. ICPR 2008. 19th International Conference of the IEEE, pp.1-4, 2008.
[7] Ivan Laptev, Tony Lindeberg, “Tracking of Multi-state Hand Models Using Particle Filtering and a Hierarchy of Multi-scale Image Features”, Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision, 2001.
[8] Sheng-Hsiang Chang,Wen-Cheng Chou and Wen-Yen Lin, “A Real-time Gesture Recognition Implementation on SoC Development Platform”, Consumer Electronics-Berlin(ICCE-Berlin) of IEEE, pp. 1-5, 2012.
[9] Jing-Ming Guo, “Improved Hand Tracking System”, Circuits and Systems for Video Technology of IEEE, pp. 693-701, 2012.
[10] Lei Geng, “Real Time Foreground-Background Segmentation Using Two-Layer Codebook Model”, Control, Automation and Systems Engineering (CASE)of IEEE, pp. 1-5, 2011.
[11] Bruzzone, L. “A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images”, Geoscience and Remote Sensing of IEEE, pp. 2587-2600, 2006.
[12] C. Stauffer and W. E. L Grimson, “Adaptive background mixture models for real-time tracking”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
[13] Chen, S., and Haralick, R.M., “Recursive Erosion, Dilation, Opening, and Closing Transforms”, Image Processing, IEEE Transactions on(Volume: 4 ,Issue:3), pp. 335-345, 1995.
[14] F. Chang, C. J. Chen, and C. J. Lu, “A linear-time component-labeling algorithm using contour tracing technique”, Comput. Vis. Image Understand, vol. 93, pp. 206-220, 2004.
[15] Grana, C., Borghesani, D., and Cucchiara, R., “Fast block based connected components labeling”, in Proc. IEEE International Conference on Image Process, Cairo, Egypt, Nov. 2009.
[16] Caixia Deng, “An edge detection approach of image fusion based on improved Sobel operator”, Image and Signal Processing (CISP)2011 4th International of IEEE, pp.1189-1193, 2011.
[17] Meenakshi Panwar,“Hand Gesture Recognition based on Shape Parameters”, Computing, Communication and Applications (ICCCA), 2012 International Conference on, pp1-6, 2012.
[18] Mita, T. “Joint Haar-like features for face detection”, Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, pp.17-21, 2005.
[19] Shuang Wu,“Object Tracking Based on ORB and Temporal-Spacial Constraint”, Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on 18-20 Oct. 2012,pp.597-600, 2012.
[20] C.R. Wren, A. Azarbayejani, T. Darrell and A. Pentland, “Pfinder： Realtime Tracking of the Human Body, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, pp. 780-785, 1997.
[21] I. Haritaoglu, D. Harwood and L.S. Davis, “Hydra: Multiple People Detection and Tracking Using Silhouettes,” Proceedings of the Second IEEE Workshop on Visual Surveillance, pp.6-13, June 1999.
[22] Zivkovic, Z. “Improved adaptive Gaussian mixture model for background subtraction”, Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th of IEEE, vol.2, pp. 28-31, 2004.
[23] C. Stauffer and W. E. L Grimson, “Adaptive background mixture models for real-time tracking”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246-252, 1999.
[24] F. Chang, C. J. Chen, and C. J. Lu, “A linear-time component-labeling algorithm using contour tracing technique,” Comput. Vis. Image Understand, vol. 93, pp. 206–220, 2004.
[25] Simion, G. Gui. V, and Otesteanu. M, “Finger Detection Based on Hand Contour and Colour Information,” Applied Computational Intelligence and Informatics (SACI), 2011 6th IEEE International Symposium on, pp. 97-100, 2011.
[26] Yuh-Rau Wang, Wei-Hung Lin, and Ling Yang, “A Novel Real Time Hand Detection Based On Skin-Color”, 2013 IEEE 17th International Symposium on Consumer Electronics(ISCE).
[27] Keng-Pei Lin and Ming-Syan Chen,“On the Design and Analysis of the Privacy-Preserving SVM Classifier”, Knowledge and Data Engineering, IEEE Transactions on(Volume: 23, Issue: 11), pp.1704-1717, Nov. 2011.
[28] Konstantinos G. Derpanis, Erich T. H. Leung and Mikhail Sizintsev,“FAST SCALE-SPACE FEATURE REPRESENTATIONS BY GENERALIZED INTEGRAL IMAGES”, Image Processing, 2007. ICIP 2007. IEEE International Conference on (Volume: 4 ), Sept. 16 2007-Oct. pp. IV - 521 - IV – 524, 2007.
[29] Qing Chen, Nicolas D. Georganas, Emil M. Petriu,“Real-time Vision-based Hand Gesture Recognition Using Haar-like Features”, Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE, 1-3 May 2007, pp. 1-7, 2007.
[30] Cao Chuqing,Li Ruifeng,“Real-Time Hand Posture Recognition Using Haar-Like and Topological Feature”, Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on 24-25 April 2010,pp. 683-687, 2010.
[31] Zhang Huijuan ,Hu Qiong,“Fast Image Matching Based-on Improved SURF Algorithm”, Electronics, Communications and Control (ICECC), 2011 International Conference on, 9-11 Sept. 2011,pp.1460-1463, 2011.
[32] Majumdar, A., “Discriminative SIFT features for face recognition”, Electrical and Computer Engineering, 2009. CCECE 09 of IEEE, pp. 27-30, 2009..
[33] Lisha Guo,Junshan Li, YingHong Zhu1, ZongQi Tang,“A Novel Features from Accelerated Segment Test Algorithm Based on LBP on Image Matching”, Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on, 27-29 May 2011, pp.355-358, 2011.
[34] M. Calonder, V. Lepetit, C. Strecha, and P. Fua., “Brief： Binary robust independent elementary features”, In European Conference on Computer Vision, 2010.