Abstract
In recent years, depth cameras have become a widely available sensor type that captures depth images at real-time frame rates. For example, Microsoft KINECT is a powerful but cheap device to get depth images. Even though recent approaches have shown that 3D pose estimation and recognition from monocular 2.5D depth images has become feasible, there are still some challenge problems like gesture detection and recognition. In this paper, we propose a gesture recognition method and use that to make a puzzle game with kinesthetic system. Gesture is very important for our system because instead of using some devices like keyboard or mouse, users will play the puzzle game by their own hand. We will focus on using ROI and some algorithms that we proposed to do gesture detection and recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Albitar C, Graebling P (2007) Christophe DOIGNON robust structured light coding for 3D reconstruction computer vision. International conference on computer vision (ICCV)
Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images. In: Proceeding of the IEEE conference on computer vision and pattern recognition (CVPR)
Girshick R, Shotton J, Kohli P, Criminisi A, Fitzgibbon A (2011) Efficient regression of general-activity human poses from depth images. In: Proceeding of international conference on computer vision (ICCV)
Grest D, Woetzel J, Koch R (2005) Nonlinear body pose estimation from depth images. In: Proceeding DAGM
Ye G, Liu Y, Hasler N, Ji X, Dai Q, Theobalt C (2012) Performance capture of interacting characters with handheld kinects. In: Proceeding of European conference on computer vision (ECCV)
Abramov A, Pauwels K, Papon J, Worgotter F, Dellen B (2012) Depth-supported real-time video segmentation with the Kinect. In: IEEE workshop on applications of computer vision (WACV)
Baak A, Muller M, Bharaj G, Seidel HP, Theobalt C (2011) A data-driven approach for real-time full body pose reconstruction from a depth camera. In: Proceeding of international conference on computer vision (ICCV)
Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohli P, Shotton J, Hodges S, Fitzgibbon A (2011) KinectFusion: real-time dense surface mapping and tracking. In: Proceeding of IEEE international symposium on mixed and augmented reality (ISMAR), pp 127–136
Frati V, Prattichizzo D (2011) Using Kinect for hand tracking and rendering in wearable haptics. World haptics conference (WHC). Dipt. di Ing. dell’’Inf., Univ. di Siena, Siena, Italy
Raheja JL (2011) Tracking of fingertips and centers of palm using KINECT. Computational intelligence, modelling and simulation (CIMSiM)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Chuang, CH., Chen, YN., Deng, MS., Fan, KC. (2014). Gesture Recognition Based on Kinect. In: Huang, YM., Chao, HC., Deng, DJ., Park, J. (eds) Advanced Technologies, Embedded and Multimedia for Human-centric Computing. Lecture Notes in Electrical Engineering, vol 260. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7262-5_128
Download citation
DOI: https://doi.org/10.1007/978-94-007-7262-5_128
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7261-8
Online ISBN: 978-94-007-7262-5
eBook Packages: EngineeringEngineering (R0)