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研究生： 洪健涵
Chien-Han Hung
論文名稱： 基於LBP與WLD混合型特徵之輪軸機器人手勢操作系統
Gesture Recognition Based on LBP and WLD Fusion Features for Mobile Robot Control Systems
指導教授： 林昌鴻
Chang-Hong Lin
口試委員: 陳維美
none
林敬舜
none
沈中安
none
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 64
中文關鍵詞： 行動機器人 、人機互動 (HRI) 、手勢識別 、機器學習 、哈爾小波轉換 (HWT) 、韋伯局部描述子 (WLD) 、局部二值模式 (LBP) 、支持向量機 (SVM)
外文關鍵詞： Mobile robot, Human Robot Interaction (HRI), hand gestures recognition, machine learning, Haar Wavelet Transform (HWT), Weber Local Description (WLD), Local Binary Pattern (LBP), Support Vector Machine (SVM)
相關次數： 點閱：290  下載：3 
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    • 嵌入式裝置近期內不管在娛樂、醫療保健和工業應用的地位都日趨重要，尤其各類機器人系統更是蓬勃發展，使用上也更貼近現實生活中的需求，此外人機互動 (Human Robot Interaction，簡稱HRI) 的領域更是因為嵌入式裝置及各式數位影像處理的不斷擴展而在學術界與工業界都獲得良好且快速的成長，致使此領域在各方面都獲得了更好的發揮舞台。本研究基於顏色和深度攝影機來實現一套支援手勢識別的導航行動機器人系統，在技術上，本系統透過三種不同的實作手勢來變化出5種不同的指令並傳送到ARM的嵌入式裝置以進行行動機器人的控制，在導航的控制部分，基於前人所提出的RGB-H-CrCb色彩空間模型以顏色區塊分割來找出手的部位，並計算其雙手偏離的角度來當作行動機器人旋轉的控制參數，而本論文所提出的手勢特徵提取方式先透過哈爾小波轉換 (Haar Wavelet Transform，簡稱HWT) 得到保有大部份資訊的低頻部分及保有紋理資訊的高頻部分，再分別結合韋伯局部描述子 (Weber Local Descriptor，簡稱WLD) 與局部二值模式 (Local Binary Pattern，簡稱LBP) 來取得所需之特徵資訊，而系統中手勢識別的分類器是採用支持向量機 (Support Vector Machine，簡稱SVM) 中的線性內核演算法來進行手勢的判別，系統實作中也提供了較人性的圖形化介面以提高此裝置的使用效率。

    This thesis researches and implements the hand recognition based navigation mobile robot systems using color and depth cameras. Embedded devices recently play a more important role in some real-life applications, such as the entertainment, health care, and industrial applications, in particular, various types of mobile robot systems is booming faster. Furthermore, Human Robot Interaction (HRI) has drawn the great improvement in not only academic but also industrial applications. The embedded devices and all kinds of digital image processing methods have gained a good and rapid growth, resulting in this field in every stage to get a better play. In the navigation control, we create the present system in five different command through three different hand gesture and transfer these commands to ARM embedded devices for controlling the mobile robot's action. The hand detection based human’s face is implemented from skin segmentation with the RGB-H-CrCb color model. The deviation angle between left and right hands is choosing to control the rotation of robot. After the hand region has been fetched, the proposed method uses the Haar Wavelet Transform (HWT) to get the low-frequency part and high-frequency part, which contains most of the information and the texture information respectively. Then we combine it with the Weber Local Description (WLD) and Local Binary Pattern (LBP) to obtain the desired gesture features. In addition, the hand gesture classifier is Support Vector Machine (SVM) to control the linear motion. Implementation system also provides a more user-friendly graphical interface to improve the efficiency of the use of this device.

    LIST OF CONTENTS 摘要 ABSTRACT II 致謝 III LIST OF CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VIII CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Contribution 2 1.3 Thesis Organization 3 CHAPTER 2 RELATED WORKS 4 2.1 Human Gestures and Behaviors with Mobile Robots 4 2.2 Local binary Pattern and Weber Law Description 6 CHAPTER 3 SYSTEM OVERVIEW 8 3.1 Introduction of RGB-D Camera Technologies 8 3.2 Mobile Robot Architecture 13 3.3 Graphic User Interface and Vision Based Control Rules 14 3.4 Hand Region Detection 18 CHAPTER 4 PROPOSED METHOD 27 4.1 Haar Wavelet Transform 28 4.2 Local Binary Pattern 32 4.3 Weber Local Description 34 4.4 Feature Histogram Fusion 37 4.5 Support Vector Machine 39 CHAPTER 5 EXPERIMENTAL RESULTS 40 5.1 Recognition Rate and Average Run Time Comparison 41 5.2 Processing Time Comparison 43 5.3 System Feasibility 43 5.4 Caution Functions 46 CHAPTER 6 CONCLUSIONS and Future works 47 6.1 Conclusions 47 6.2 Future works 48 REFERENCES 49  

    CHAPTER 7 REFERENCES

    [1] H. Zhang, C. Reardon and L. E. Parker, "Real-Time Multiple Human Perception With Color-Depth Cameras on a Mobile Robot," IEEE Transactions on Cybernetics, vol. 43, no. 5, pp. 1429 - 1441, 2013.
    [2] F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers and W. Burgard, "An evaluation of the RGB-D SLAM system," in IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, 2012.
    [3] "AlphaDog: http://www.pcmag.com/article2/0,2817,2409628,00.asp," [Online].
    [4] "ASUS Zenbo: https://zenbo.asus.com/," [Online].
    [5] "Dancing Robot: http://www.dnp.co.jp/news/10104464_2482.html," [Online].
    [6] J. Kovac, P. Peer and F. Solina, "Human skin color clustering for face detection," in The IEEE Region 8 EUROCON International Conference on Computer as a Tool., 2003.
    [7] M.-Q. Do and C.-H. Lin, "Embedded human-following mobile-robot with an RGB-D camera," in International Conference on Machine Vision Applications (MVA), Tokyo, 2015.
    [8] S. Salah, H. Du and N. Al-Jawad, "Fusing Local Binary Patterns with Wavelet Features for Ethnicity Identification," in International Conference on Signal and Image Processing (ICSIP), 2013.
    [9] J. Chen, S. Shan, C. He, G. Zhao, M. Pietikainen, X. Chen and W. Gao, "WLD: A Robust Local Image Descriptor," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1705 - 1720, 2009.
    [10] I. Ullah, M. Hussain, H. Aboalsamh, G. Muhammad, A. M. Mirza and G. Bebis, "Gender Recognition from Face Images with Dyadic Wavelet Transform and Local Binary Pattern," Advances in Visual Computing, pp. 409-419 , Volume 7432, 2012.
    [11] C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, p. 273–297, 1995.
    [12] H. Bouraoui, A. Khamis and F. Krray, "A testbed platform for assessing human-robot verbal interaction," in International Conference on Autonomous and Intelligent Systems (AIS), Povoa de Varzim, 2010.
    [13] F. Faber, M. Bennewitz, C. Eppner, A. Gorog, C. Gonsior, D. Joho, M. Schreiber and S. Behnke, "The humanoid museum tour guide Robotinho," in IEEE International Symposium on Robot and Human Interactive Communication, Toyama, 2009.
    [14] A. Punchihewa and Z. M. Arshad, "Voice command interpretation for robot control," in 5th International Conference on Automation, Robotics and Applications (ICARA), Wellington, 2011.
    [15] M. T. Wolf, C. Assad, M. T. Vernacchia, J. Fromm and H. L. Jethani, "Gesture-based robot control with variable autonomy from the JPL BioSleeve," in IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, 2013.
    [16] J. L. Raheja, R. Shyam, U. Kumar and P. B. Prasad, "Real-Time Robotic Hand Control Using Hand Gestures," in Second International Conference on Machine Learning and Computing (ICMLC), Bangalore, 2010.
    [17] M. Hasanuzzaman, V. Ampornaramveth, T. Zhang, M. A. Bhuiyan, Y. Shirai and H. Ueno, "Real-time Vision-based Gesture Recognition for Human Robot Interaction," in IEEE International Conference on Robotics and Biomimetics, Shenyang, 2004.
    [18] R. C. Luo and Y.-C. Wu, "Human-robot interaction with multi-sensor fusion based hand sign recognition for service robot," in 38th Annual Conference on IEEE Industrial Electronics Society (IECON), Montreal, 2012.
    [19] S. Takahashi, Y. Takahashi, Y. Maeda and T. Nakamura, "Development of body mapping from human demonstrator to inverted-pendulum mobile robot for imitation," in IEEE International Conference on Fuzzy Systems (FUZZ), Taipei, 2011.
    [20] P. Molchanov, S. Gupta, K. Kim and K. Pulli, "Multi-sensor system for driver's hand-gesture recognition," in 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, 2015.
    [21] D. Harwood, T. Ojala, M. Pietikäinen, S. Kelman and L. Davis, "Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions," Pattern Recognition Letters, vol. 16, no. 1, pp. 1 - 10, 1995.
    [22] T. Ojala, M. Pietikäinen and D. Harwood, "Comparative study of texture detection and classification algorithms," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
    [23] 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.
    [24] A. K. Jain, Fundamentals of digital image processing, Prentice-Hall, 1989.
    [25] J. Das and H. Roy, "Human Face Detection in Color Images Using HSV Color Histogram and WLD," in International Conference on Computational Intelligence and Communication Networks (CICN), Bhopal, 2014.
    [26] S. Lin, Y.-M. Liu and Y.-R. Jhu, "A robust image descriptor for human detection based on hog and weber's law," International Journal of Innovative Computing Information and Control, vol. 9, no. 10, pp. 3887-3901, 2013.
    [27] D. Gong, S. Li and Y. Xiang, "Face recognition using the Weber Local Descriptor," in The First Asian Conference on Pattern Recognition, Beijing, 2011.
    [28] I. Ullah, M. Hussain, G. Muhammad, H. Aboalsamh, G. Bebis and A. M. Mirza, "Gender recognition from face images with local WLD descriptor," in International Conference on Systems Signals and Image Processing (IWSSIP), 2012.
    [29] G. Muhammad, M. Hussain, F. Alenezy, A. M. Mirza, G. Bebis and H. Aboalsamh, "Race recognition using local descriptors," in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Kyoto, 2012.
    [30] "Bumblebee camera: http://www.ptgrey.com/bumblebee-xb3-1394b-stereo-vision-camera-systems-2," [Online].
    [31] "Kinect: http://www.xbox.com/en-US/xbox-360/accessories/kinect," [Online].
    [32] "pmd [vision]® CamCube 3.0: http://www.pmdtec.com/news_media-/video/camcube.php [Online].," [Online].
    [33] "Asus Xtion Pro Live website: http://www.asus.com-/Commercial_3D_Sensor-/Xtion_PRO_LIVE," [Online].
    [34] "ROS-Introduction: http://wiki.ros.org/ROS/Introduction," [Online].
    [35] J. Blanchette and M. Summerfield, C++ GUI Programming with Qt4, Prentice Hall, 2006.
    [36] L. Issolio and E. M. Colombo, "Brightness for different surround conditions: The effect of transient glare," Perception and Psychophysics, pp. 702-709, 2006.
    [37] Y. Zhu and B. Yuan, "Real-time hand gesture recognition with Kinect for playing racing video games," in International Joint Conference on Neural Networks (IJCNN), Beijing, 2014.
    [38] J. Ruiz-del-Solar and R. Verschae, "Skin detection using neighborhood information," in IEEE International Conference on Automatic Face and Gesture Recognition, 2004.
    [39] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    [40] D. G. R. Bradski and A. Kaehler, Learning opencv, 1st edition, O'Reilly Media, Inc., 2008.
    [41] T. S. Lee, "Image representation using 2D Gabor wavelets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 959 - 971, 2002.
    [42] Q. K. Trinh and P. Z. Fan, "Construction of multilevel Hadamard matrices with small alphabet," Electronics Letters, vol. 44, no. 21, pp. 1250-1252, 2008.
    [43] I. Meyer, Wavelets and operators. Vol.1, Cambridge University Press, 1995.
    [44] H. Zhang and G. Chen, "The Research of Face Recognition Based on PCA and K-Nearest Neighbor," in Symposium on Photonics and Optoelectronics (SOPO), Shanghai, 2012.
    [45] M. Agarwal, H. Agrawal, N. Jain and M. Kumar, "Face Recognition Using Principle Component Analysis, Eigenface and Neural Network," in 2010, Bangalore, International Conference on Signal Acquisition (ICSAP) .
    [46] Y.-T. Chen and K.-T. Tseng, "Multiple-angle Hand Gesture Recognition by Fusing SVM Classifiers," in IEEE International Conference on Automation Science and Engineering, Scottsdale, 2007.
    [47] D.-Y. Huang, W.-C. Hu and S.-H. Chang, "Vision-Based Hand Gesture Recognition Using PCA+Gabor Filters and SVM," in Fifth International Conference on Intelligent Information Hiding and Multimedia Signal , Kyoto, 2009.

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