TY - GEN
T1 - A new approach for dynamic gesture recognition using skeleton trajectory representation and histograms of cumulative magnitudes
AU - Escobedo, Edwin
AU - Camara, Guillermo
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/10
Y1 - 2017/1/10
N2 - In this paper, we present a new approach for dynamic hand gesture recognition that uses intensity, depth, and skeleton joint data captured by Kinect sensor. This method integrates global and local information of a dynamic gesture. First, we represent the skeleton 3D trajectory in spherical coordinates. Then, we select the most relevant points in the hand trajectory with our proposed method for keyframe detection. After, we represent the joint movements by spatial, temporal and hand position changes information. Next, we use the direction cosines definition to describe the body positions by generating histograms of cumulative magnitudes from the depth data which were converted in a point-cloud. We evaluate our approach with different public gesture datasets and a sign language dataset created by us. Our results outperformed state-of-the-art methods and highlight the smooth and fast processing for feature extraction being able to be implemented in real time.
AB - In this paper, we present a new approach for dynamic hand gesture recognition that uses intensity, depth, and skeleton joint data captured by Kinect sensor. This method integrates global and local information of a dynamic gesture. First, we represent the skeleton 3D trajectory in spherical coordinates. Then, we select the most relevant points in the hand trajectory with our proposed method for keyframe detection. After, we represent the joint movements by spatial, temporal and hand position changes information. Next, we use the direction cosines definition to describe the body positions by generating histograms of cumulative magnitudes from the depth data which were converted in a point-cloud. We evaluate our approach with different public gesture datasets and a sign language dataset created by us. Our results outperformed state-of-the-art methods and highlight the smooth and fast processing for feature extraction being able to be implemented in real time.
KW - direction cosines
KW - global and local features
KW - hand gesture recognition
KW - histogram of cumulative magnitudes
KW - keyframes
KW - spherical coordinate system
UR - http://www.scopus.com/inward/record.url?scp=85013790237&partnerID=8YFLogxK
U2 - 10.1109/SIBGRAPI.2016.037
DO - 10.1109/SIBGRAPI.2016.037
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85013790237
T3 - Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016
SP - 209
EP - 216
BT - Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016
Y2 - 4 October 2016 through 7 October 2016
ER -