TY - GEN
T1 - A dynamic hand gesture recognition model based on the improved dynamic time warping algorithm
AU - Li, Yi
AU - Feng, Xuan
AU - Xu, Yuanping
AU - Dong, Xude
AU - Xu, Zhijie
AU - Huang, Jian
AU - Lu, Li
N1 - Publisher Copyright:
© 2019 Chinese Automation and Computing Society in the UK-CACSUK.
PY - 2019/9
Y1 - 2019/9
N2 - In order to recognize dynamic hand gestures with an effective and intelligent manner, this study proposes an integrated dynamic hand gesture recognition model based on the improved DTW (Dynamic Time Warping) algorithm that has a significant impact on the efficiency of dynamic trajectory analysis. The proposed model is divided into three operational steps: 1) using the three-frame difference method to track the gesture motion area by separating the dynamic motion region from the skin-like background; 2) applying the Hu-moment method to locate feature points from the extracted gesture motion region, and then use them to describe the motion trajectory; 3) exploring an improved DTW algorithm for gesture template match. Traditional DTW algorithm has excessive computational complexity and low operational efficiency. This study improved the running efficiency of DTW without reducing matching accuracy. Benchmarking experiments carried out on identifying the six classic dynamic gestures have yielded satisfactory classification results.
AB - In order to recognize dynamic hand gestures with an effective and intelligent manner, this study proposes an integrated dynamic hand gesture recognition model based on the improved DTW (Dynamic Time Warping) algorithm that has a significant impact on the efficiency of dynamic trajectory analysis. The proposed model is divided into three operational steps: 1) using the three-frame difference method to track the gesture motion area by separating the dynamic motion region from the skin-like background; 2) applying the Hu-moment method to locate feature points from the extracted gesture motion region, and then use them to describe the motion trajectory; 3) exploring an improved DTW algorithm for gesture template match. Traditional DTW algorithm has excessive computational complexity and low operational efficiency. This study improved the running efficiency of DTW without reducing matching accuracy. Benchmarking experiments carried out on identifying the six classic dynamic gestures have yielded satisfactory classification results.
KW - Data Gloves
KW - Dynamic Time Warping
KW - Hand Gesture Recognition
KW - Trajectory Analysis
UR - http://www.scopus.com/inward/record.url?scp=85075802953&partnerID=8YFLogxK
U2 - 10.23919/IConAC.2019.8895002
DO - 10.23919/IConAC.2019.8895002
M3 - Conference Proceeding
AN - SCOPUS:85075802953
T3 - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
BT - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
A2 - Yu, Hui
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Automation and Computing, ICAC 2019
Y2 - 5 September 2019 through 7 September 2019
ER -