TY - GEN
T1 - A robust gesture recognition using hand local data and skeleton trajectory
AU - Escobedo-Cardenas, E.
AU - Camara-Chavez, G.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - In this paper, we propose a new approach for dynamic hand gesture recognition using intensity, depth and skeleton joint data captured by KinectTM sensor. The proposed approach integrates global and local information of a dynamic gesture. First, we represent the skeleton 3D trajectory in spherical coordinates. Then, we extract the key frames corresponding to the points with more angular and distance difference. In each key frame, we calculate the spherical distance from the hands, wrists and elbows to the shoulder center, also we record the hands position changes to obtain the global information. Finally, we segment the hands and use SIFT descriptor on intensity and depth data. Then, Bag of Visual Words (BOW) approach is used to extract local information. The system was tested with the ChaLearn 2013 gesture dataset and our own Brazilian Sign Language dataset, achieving an accuracy of 88.39% and 98.28%, respectively.
AB - In this paper, we propose a new approach for dynamic hand gesture recognition using intensity, depth and skeleton joint data captured by KinectTM sensor. The proposed approach integrates global and local information of a dynamic gesture. First, we represent the skeleton 3D trajectory in spherical coordinates. Then, we extract the key frames corresponding to the points with more angular and distance difference. In each key frame, we calculate the spherical distance from the hands, wrists and elbows to the shoulder center, also we record the hands position changes to obtain the global information. Finally, we segment the hands and use SIFT descriptor on intensity and depth data. Then, Bag of Visual Words (BOW) approach is used to extract local information. The system was tested with the ChaLearn 2013 gesture dataset and our own Brazilian Sign Language dataset, achieving an accuracy of 88.39% and 98.28%, respectively.
KW - global and local information
KW - hand gesture recognition
KW - key frames
KW - spherical coordinate system
UR - http://www.scopus.com/inward/record.url?scp=84956644385&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7350998
DO - 10.1109/ICIP.2015.7350998
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:84956644385
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1240
EP - 1244
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -