Abstract
Computers and other electronic devices shrink and the need for a human interface remains. This generates a tremendous interest in alternative interfaces such as touch-less gesture interfaces, which can create a large, generic interface with a small piece of hardware. However, the acceptance of novel interfaces is hard to predict and may challenge the required computer-vision algorithms in terms of robustness, latency, precision, and the complexity of the problems involved.
In this article, we provide an overview of current gesture interfaces that are based on depth sensors. The focus is on the algorithms and systems that operate in the near range and can recognize hand gestures of increasing complexity, from simple wipes to the tracking of a full hand-skeleton.
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References
Pavlovic, V., Sharma, R., Huang, T.: Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 677–695 (1997)
Haker, M., Böhme, M., Martinetz, T., Barth, E.: Deictic gestures with a time-of-flight camera. In: Kopp, S., Wachsmuth, I. (eds.) GW 2009. LNCS, vol. 5934, pp. 110–121. Springer, Heidelberg (2010)
Droeschel, D., Stuckler, J., Behnke, S.: Learning to interpret pointing gestures with a time-of-flight camera. In: 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 481–488 (2011)
Kolb, A., Barth, E., Koch, R., Larsen, R.: Time-of-flight cameras in computer graphics. Computer Graphics Forum 29(1), 141–159 (2010)
Böhme, M., Haker, M., Martinetz, T., Barth, E.: A facial feature tracker for human-computer interaction based on 3D Time-of-Flight cameras. International Journal of Intelligent Systems Technologies and Applications 5(3/4), 264–273 (2008)
Haker, M., Böhme, M., Martinetz, T., Barth, E.: Self-organizing maps for pose estimation with a time-of-flight camera. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 142–153. Springer, Heidelberg (2009)
Böhme, M., Haker, M., Martinetz, T., Barth, E.: Head tracking with combined face and nose detection. In: Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), Iaşi, Romania (2009)
Böhme, M., Haker, M., Riemer, K., Martinetz, T., Barth, E.: Face detection using a time-of-flight camera. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 167–176. Springer, Heidelberg (2009)
Böhme, M., Haker, M., Martinetz, T., Barth, E.: Shading constraint improves accuracy of time-of-flight measurements. Computer Vision and Image Understanding 114, 1329–1335 (2010)
Holte, M., Moeslund, T., Fihl, P.: View-invariant gesture recognition using 3D optical flow and harmonic motion context. Computer Vision and Image Understanding 114(12), 1353–1361 (2010), Special issue on Time-of-Flight Camera Based Computer Vision
Haubner, N., Schwanecke, U., Dorner, R., Lehmann, S., Luderschmidt, J.: Recognition of dynamic hand gestures with time-of-flight cameras. In: Dörner, R., Krömker, D. (eds.) Self Integrating Systems for Better Living Environments: First Workshop, Sensyble. Number 1, pp. 7–13 (2010)
Kollorz, E., Penne, J., Hornegger, J., Barke, A.: Gesture recognition with a time-of-flight camera. Int. J. Intell. Syst. Technol. Appl. 5(3/4), 334–343 (2008)
Mo, Z., Neumann, U.: Real-time hand pose recognition using low-resolution depth images. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1499–1505 (2006)
Suryanarayan, P., Subramanian, A., Mandalapu, D.: Dynamic hand pose recognition using depth data. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3105–3108 (August 2010)
Keskin, C., Kirac, F., Kara, Y., Akarun, L.: Randomized decision forests for static and dynamic hand shape classification. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 31–36 (2012)
Keskin, C., Kirac, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision. Advances in Computer Vision and Pattern Recognition, pp. 119–137. Springer, London (2013)
Kurakin, A., Zhang, Z., Liu, Z.: A real time system for dynamic hand gesture recognition with a depth sensor. In: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp. 1975–1979 (2012)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3D tracking of hand articulations using kinect. In: British Machine Vision Conference, Dundee, UK, vol. 2 (2011)
Doliotis, P., Athitsos, V., Kosmopoulos, D., Perantonis, S.: Hand shape and 3D pose estimation using depth data from a single cluttered frame. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part I. LNCS, vol. 7431, pp. 148–158. Springer, Heidelberg (2012)
Ren, Z., Yuan, J., Zhang, Z.: Robust hand gesture recognition based on finger-earth mover’s distance with a commodity depth camera. In: Proceedings of the 19th ACM International Conference on Multimedia (MM 2011), pp. 1093–1096. ACM, New York (2011)
Caputo, M., Denker, K., Dums, B., Umlauf, G.: 3D hand gesture recognition based on sensor fusion of commodity hardware. In: Reiterer, H., Deussen, O. (eds.) Mensch & Computer 2012: Interaktiv Informiert Allgegenwärtig und Allumfassend!?, München (Oldenbourg Verlag), pp. 293–302 (2012)
Reyes, M., Dominguez, G., Escalera, S.: Feature weighting in dynamic time warping for gesture recognition in depth data. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1182–1188 (2011)
Zetzsche, C., Barth, E., Wegmann, B.: The importance of intrinsically two-dimensional image features in biological vision and picture coding. In: Watson, A.B. (ed.) Digital Images and Human Vision, pp. 109–138. MIT Press (October 1993)
Erol, A., Bebis, G., Nicolescu, M., Boyle, R.D., Twombly, X.: Vision-based hand pose estimation: A review. Computer Vision and Image Understanding 108(12), 52–73 (2007), Special Issue on Vision for Human-Computer Interaction
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Campbell, L., Becker, D., Azarbayejani, A., Bobick, A., Pentland, A.: Invariant features for 3-D gesture recognition. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 157–162 (1996)
Ballan, L., Taneja, A., Gall, J., Van Gool, L., Pollefeys, M.: Motion capture of hands in action using discriminative salient points. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 640–653. Springer, Heidelberg (2012)
Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Markerless and efficient 26-DOF hand pose recovery. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 744–757. Springer, Heidelberg (2011)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)
Lahamy, H., Litchi, D.: Real-time hand gesture recognition using range cameras. In: Canadian Geomatics Conference (CGC), vol. 10 (2010)
Malassiotis, S., Tsalakanidou, F., Mavridis, N., Giagourta, V., Grammalidis, N., Strintzis, M.: A face and gesture recognition system based on an active stereo sensor. In: Proceedings of the 2001 International Conference on Image Processing, vol. 3, pp. 955–958 (2001)
Breuer, P., Eckes, C., Müller, S.: Hand gesture recognition with a novel IR time-of-flight range camera–A pilot study. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 247–260. Springer, Heidelberg (2007)
Zhu, X., Wong, K.Y.K.: Single-frame hand gesture recognition using color and depth kernel descriptors. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2989–2992 (November 2012)
Liu, X., Fujimura, K.: Hand gesture recognition using depth data. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 529–534 (2004)
Van den Bergh, M., Van Gool, L.: Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 66–72 (2011)
Trindade, P., Lobo, J., Barreto, J.: Hand gesture recognition using color and depth images enhanced with hand angular pose data. In: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 71–76 (2012)
Ghobadi, S., Loepprich, O., Hartmann, K., Loffeld, O.: Hand segmentation using 2D/3D images. In: Cree, M.J. (ed.) IVCNZ 2007 Conference, University of Waikato, pp. 64–69 (2007)
Holte, M., Moeslund, T., Fihl, P.: Fusion of range and intensity information for view invariant gesture recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2008), pp. 1–7 (2008)
Uebersax, D., Gall, J., Van den Bergh, M., Van Gool, L.: Real-time sign language letter and word recognition from depth data. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 383–390 (2011)
Hernandez-Vela, A., Bautista, M., Perez-Sala, X., Ponce, V., Baro, X., Pujol, O., Angulo, C., Escalera, S.: BoVDW: Bag-of-visual-and-depth-words for gesture recognition. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 449–452 (2012)
Song, Y., Demirdjian, D., Davis, R.: Multi-signal gesture recognition using temporal smoothing hidden conditional random fields. In: 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 388–393 (2011)
Haker, M., Barth, E., Martinetz, T.: Method for the real-time-capable, computer-assisted analysis of an image sequence containing a variable pose, International patent WO/2010/130245 (filed: May 6, 2010)
Andersen, M.R., Jensen, T., Lisouski, P., Mortensen, A.K., Hansen, M.K., Gregersen, T., Ahrendt, P.: Kinect depth sensor evaluation for computer vision applications. Technical report ECE-TR-6, Department of Engineering Electrical and Computer Engineering, Aarhus University (2012)
State, A., Coleca, F., Barth, E., Martinetz, T.: Hand tracking with an extended self-organizing map. In: Estevez, P.A., Principe, J.C., Zegers, P. (eds.) Advances in Self-Organizing Maps. AISC, vol. 198, pp. 115–124. Springer, Heidelberg (2013)
Coleca, F., Klement, S., Martinetz, T., Barth, E.: Real-time skeleton tracking for embedded systems. In: Proceedings of Multimedia Content and Mobile Devices SPIE Conference, vol. 8667 (2013)
Guan, H., Feris, R., Turk, M.: The isometric self-organizing map for 3D hand pose estimation. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006), pp. 263–268 (2006)
Caridakis, G., Karpouzis, K., Drosopoulos, A., Kollias, S.: Somm: Self organizing markov map for gesture recognition. Pattern Recognition Letters 31(1), 52–59 (2010)
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Coleca, F., Martinetz, T., Barth, E. (2013). Gesture Interfaces with Depth Sensors. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds) Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Lecture Notes in Computer Science, vol 8200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44964-2_10
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DOI: https://doi.org/10.1007/978-3-642-44964-2_10
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