Feature design scheme for Kinect-based DTW human gesture recognition
- 456 Downloads
Feature selection is a crucial factor in Kinect-based pattern recognition, including common human gesture recognition. For Kinect-based human gesture recognition, the information contained in the feature extracted for gesture recognition is conventionally the (x,y,z) coordinates of the primary joints in the human body. However, such traditionally used feature information containing only joint positions is apparently insufficient for clearly describing the characteristics of human activity patterns. This paper proposes a feature design scheme involving hybridizations of joint positions and joint angles for human gesture recognition with the Kinect camera. The presented feature design method effectively hybridizes the 20 main human joint positions captured by the Kinect camera and the joint angle information of 12 critical joints, along with significant angle variations when a gesture is made. The method is employed in dynamic time warping (DTW) gesture recognition. When the proposed feature design method is used for Kinect-based DTW human gesture recognition, it derives an appropriately sized feature vector for each of the gesture categories in the DTW-referenced template database according to the activity characteristics of a certain category of gestures. Experiments on Kinect-based DTW gesture recognition involving 14 common categories of human gestures show that the feature determined using the proposed approach is superior to that obtained using the conventional approach, which considers only the joint position information.
KeywordsKinect camera Human gesture recognition Gesture feature DTW Recognition performance
This research is partially supported by the Ministry of Science and Technology (MOST) in Taiwan under Grant MOST 103-2221-E-150-046.
- 3.Ding IJ (2013) Speech recognition using variable-length frame overlaps by intelligent fuzzy control. J Intell Fuzzy Syst 25(1):49–56Google Scholar
- 4.Ding IJ, Chang CW An eigenspace-based method with a user adaptation scheme for human gesture recognition by using Kinect 3D data. Appl Math Model. doi: 10.1016/j.apm.2014.12.054
- 5.Ding IJ, Chang CW An adaptive hidden Markov model-based gesture recognition approach using Kinect to simplify large-scale video data processing for humanoid robot imitation. Multimed Tools Appl. doi: 10.1007/s11042-015-2505-9
- 7.Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft Kinect sensor: a review. IEEE Trans Cybern 43(5):2168–2267Google Scholar
- 9.Qian K, Niu J, Yang H (2013) Developing a gesture based remote human-robot interaction system using Kinect. Int J Smart Home 7(4):203–208Google Scholar
- 12.Su CJ, Huang JY, Huang SF (2012) Ensuring home-based rehabilitation exercise by using Kinect and fuzzified dynamic time warping algorithm. Proc. the Asia Pacific Industrial Engineering & Management Systems Conference, pp. 884–895Google Scholar
- 14.Wu J, Konrad J, Ishwar P (2013) Dynamic time warping for gesture-based user identification and authentication with Kinect. Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2371–2375Google Scholar
- 16.Zhang L, Hsieh JC, Wang J (2012) A Kinect-based golf swing classification system using HMM and neuro-fuzzy. Proc. IEEE International Conference on Computer Science and Information Processing (CSIP), pp. 1163–1166Google Scholar