Hand recognition using depth cameras

Alexander Cardona

Abstract


Hand position and gesture recognition from an image stream is a topic of relevance for developing human-machine interactions. The advent of low-cost cameras in the consumer market, like Microsoft Kinect, leaves open the possibility of build recognition applications, which are not affected by low light conditions. This paper is a survey of the literature on hand position and gesture recognition with the use of depth cameras. It is noticeable that reviewed papers focus on the recognition of one-handed gestures and their classification among a finite set of gestures. Last year’s advances in hand processing include techniques for posture recognition without restrictions, but it is unknown their effectiveness on low-cost hardware because testing were done without a standardized set of images and with a diversity of hardware.

Keywords


Depth image; Gesture; Tracking

Full Text:

PDF HTML

References


P. Sonwalkar, T. Sakhare, A. Patil y S. Kale, “Hand Gesture Recognition for Real Time Human Machine Interaction System”, International Journal of Engineering Trends and Technology (IJETT), vol. 19, nº 5, pp. 262-264, 2015.

P. Srilatha y T. Saranya, “Advancements in Gesture Recognition Technology”, IOSR Journal of VLSI and Signal Processing (IOSR-JVSP), vol. 4, nº 4, pp. 1-7, 2014.

P. S. S. Shaikh, S. L. Dhebe, P. D. Zambare, A. D. Jivanwal y P. P. Luniya, “Human Computer Interaction (Robot Handling) using Hand Gesture Recognition”, International Journal of Engineering Research and Technology (IJERT), vol. 3, nº 2, pp. 712-715, 2014.

J. Suarez y R. R. Murphy, “Hand gesture recognition with depth images: A review”, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 411-417, sep 2012.

Y. F. A. Gaus y F. Wong, “Hidden Markov Model-Based Gesture Recognition with Overlapping Hand-Head/Hand-Hand Estimated Using Kalman Filter”, 2012 Third International Conference on Intelligent Systems Modelling and Simulation, pp. 262-267, 2012.

Y.-c. H. Y.-s. Huang Chen, “An Occlusion-Resolving Hand Tracking Method”, 7th International Conference on Ubi-Media Computing and Workshops, pp. 276-280, 2014.

R. Hartanto, “Real Time Hand Gesture Movements Tracking and Recognizing System”, 2014 Electrical Power, Electronics, Communications, Controls, and Informatics Seminar (EECCIS), pp. 137-141, 2014.

M. Panwar, “Hand gesture recognition based on shape parameters”, 2012 International Conference on Computing, Communication and Applications, pp. 1-6, 2012.

L. Chen, H. Wei y J. Ferryman, “A survey of human motion analysis using depth imagery”, Pattern Recognition Letters, vol. 34, nº 15, pp. 1995-2006, nov 2013.

L. Cruz, D. Lucio y L. Velho, “Kinect and RGBD Images: Challenges and Applications”, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials, pp. 36-49, aug 2012.

J. Han, L. Shao, D. Xu y J. Shotton, “Enhanced computer vision with Microsoft Kinect sensor: A review”, IEEE Transactions on Cybernetics, vol. 43, nº 5, pp. 1318-1334, 2013.

M. Ye, Q. Zhang, L. Wang, J. Zhu y R. Yang, “A Survey on Human Motion Analysis from Depth Data”, Pattern Recognition Letters, vol. 34, nº 15, pp. 1995-2006, 2013.

H. S. Hasan y S. A. Kareem, “Human Computer Interaction for Vision Based Hand Gesture Recognition: A Survey”, 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), pp. 55-60, nov 2012.

A. Jana, Kinect for windows SDK programming guide, Birmingham, UK: Packt Publishing Ltd, 2012.

S. Falahati, OpenNI Cookbook, Birmingham, UK: Packt Publishing Ltd, 2013.

W. Engel, ShaderX3 : Advanced Rendering with DirectX and OpenGL, Hingham, MA, USA: Charles River Media / Cengage Learning, 2004.

M. Bhuyan, D. Neog y M. Kar, “Hand pose recognition using geometric features”, National Conference on Communications (NCC), 2011, pp. 0-4, 2011.

J. M. H. R. M. B. Matthias Schöder, “Real-Time Hand Tracking using Synergistic Inverse Kinematics”, IEEE International Conference on Robotics and Automation (ICRA), pp. 5447-5454, 2014.

I. Oikonomidis, N. Kyriazis y A. a. Argyros, “Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints”, Proceedings of the IEEE International Conference on Computer Vision, pp. 2088-2095, 2011.

C. Tang, Y. Ou, G. Jiang, Q. Xie y Y. Xu, “Hand tracking and pose recognition via depth and color information”, 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest, pp. 1104-1109, 2012.

A. Kurakin, “A real time system for dynamic hand gesture recognition with a depth sensor”, 20th European Signal PRocessing Conference (EUSIPCO 2012), nº Eusipco, pp. 1975-1979, 2012.

V. Frati y D. Prattichizzo, “Using Kinect for hand tracking and rendering in wearable haptics”, 2011 IEEE World Haptics Conference, WHC 2011, pp. 317-321, 2011.

C. Qian, X. Sun, Y. Wei, X. Tang y J. Sun, “Realtime and Robust Hand Tracking from Depth”, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.

H. Liang, J. Yuan y D. Thalmann, “Parsing the hand in depth images”, IEEE Transactions on Multimedia, vol. 16, nº 5, pp. 1241-1253, 2014.

H. Lahamy y D. Litchi, “Real-time hand gesture recognition using range cameras”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p. 38, 2010.

F. Dominio, M. Donadeo, G. Marin, P. Zanuttigh y G. M. Cortelazzo, “Hand gesture recognition with depth data”, Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream (ARTEMIS), pp. 9-16, 2013.

X. Suau, M. Alcoverro, A. López-Méndez, J. Ruiz-Hidalgo y J. R. Casas, “Real-time fingertip localization conditioned on hand gesture classification”, Image and Vision Computing, vol. 32, nº 8, pp. 522-532, 2014.

J. Lee, H. Gul, H. Kim, J. Kiml y H. Kim, “Interactive manipulation of 3D objects using Kinect for visualization tools in education”, 13th Internationl Conference on Control, Automation and Systems (ICCAS 2013), nº lCCAS, pp. 1220-1222, 2013.

C. Plagemann, V. Ganapathi, D. Koller y S. Thrun, “Real-time identification and localization of body parts from depth images”, 2010 IEEE International Conference on Robotics and Automation, pp. 3108-3113, may 2010.

C.-P. Chen, Y.-T. Chen, P.-H. Lee, Y.-P. Tsai y S. Lei, “Real-time hand tracking on depth images”, 2011 Visual Communications and Image Processing (VCIP), nº 1, pp. 1-4, nov 2011.

K. H. Kim, D. U. Jung, S. H. Lee y J. S. Choi, “A hand tracking framework using the 3D active tracking volume”, FCV 2013 - Proceedings of the 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, pp. 159-163, 2013.

S. Park, S. Yu, J. Kim, S. Kim y S. Lee, “3D hand tracking using Kalman filter in depth space”, EURASIP Journal on Advances in Signal Processing, vol. 2012, nº 1, p. 36, 2012.

Z. Ren, J. Yuan, J. Meng y Z. Zhang, “Robust part-based hand gesture recognition using kinect sensor”, IEEE Transactions on Multimedia, vol. 15, nº 5, pp. 1110-1120, 2013.

M. Tang, “Recognizing Hand Gestures with Microsoft's Kinect”, Computer, vol. 14, pp. 303-313, 2011.

Z. Ren, J. Yuan y Z. Zhang, “Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera”, Proceedings of the 19th ACM international conference on Multimedia, pp. 1093-1096, 2011.

Z. Ren, J. Meng y J. Yuan, “Depth Camera Based Hand Gesture Recognition and its Applications in Human-Computer-Interaction”, IEEE International Conference on Information Communication and Signal Processing, nº 1, pp. 3-7, 2011.

L. T. Nguyen, C. D. Thanh y T. N. Ba, “Contour Based Hand Gesture Recognition Using Depth Data Hand Gesture recognition”, Advanced Science and Technology Letters, vol. 29, nº Sip, pp. 60-65, 2013.

C. Xu y L. Cheng, “Efficient Hand Pose Estimation from a Single Depth Image”, 2013 IEEE International Conference on Computer Vision, pp. 3456-3462, dec 2013.

P. Trindade, J. Lobo y J. P. Barreto, “Hand gesture recognition using color and depth images enhanced with hand angular pose data”, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 71-76, 2012.

C. Keskin, F. Kirac, Y. E. Kara y L. Akarun, “Real Time Hand Pose Estimation using Depth Sensors”, IEEE International Conference on Computer Vision Workshops, pp. 1228-1234, 2011.

C. M. Oh, M. Z. Islam y C. W. Lee, “Articulated hand tracking using key poses driven particle filtering”, ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings, vol. 7, pp. 535-538, 2010.

M. De La Gorce, D. J. Fleet y N. Paragios, “Model-based 3D hand pose estimation from monocular video”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, nº 9, pp. 1793-1805, 2011.

I. Oikonomidis, N. Kyriazis y A. Argyros, “Efficient model-based 3D tracking of hand articulations using Kinect”, Procedings of the British Machine Vision Conference 2011, pp. 101.1--101.11, 2011.

I. Oikonomidis, N. Kyriazis y a. a. Argyros, “Tracking the articulated motion of two strongly interacting hands”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1862-1869, 2012.

M. Van Den Bergh y L. Van Gool, “Combining RGB and ToF cameras for real-time 3D hand gesture interaction”, 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, pp. 66-72, 2011.

K. Qian, J. Niu y H. Yang, “Developing a Gesture Based Remote Human-Robot Interaction System Using Kinect”, International Journal of Smart Home, vol. 7, nº 4, pp. 203-208, 2013.

Y. Li, “Hand gesture recognition using Kinect”, Software Engineering and Service Science (ICSESS), 2012 IEEE 3rd International Conference on, pp. 196-199, 2012.

M. Oszust y M. Wysocki, “Recognition of signed expressions observed by Kinect Sensor”, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013, pp. 220-225, 2013.

M. Zahedi y A. R. Manashty, “Robust Sign Language Recognition System Using ToF Depth Cameras”, World of Computer Science and Information Technology Journal (WCSIT), vol. 1, nº 3, p. 6, 2011.

M.-h. Hsu y T. K. Shih, “Real-Time Finger Tracking for Virtual Instruments”, 7th International Conference on Ubi-Media Computing and Workshops, pp. 133-138, 2014.

P. Molchanov, S. Gupta, K. Kim y K. Pulli, Multi-sensor System for Driver's Hand-Gesture Recognition, 2015, p. 8.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2015 TECCIENCIA