Open AccessLSTM‐based dynamic probability continuous hand gesture trajectory recognition
Author(s) -
Jian Chengfeng,
Li Junjie,
Zhang Meiyu
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0650
Subject(s) - computer science , gesture , trajectory , gesture recognition , handwriting , artificial intelligence , segmentation , speech recognition , pattern recognition (psychology) , hidden markov model , sliding window protocol , handwriting recognition , computer vision , feature extraction , window (computing) , physics , astronomy , operating system
In the field of continuous hand‐gesture trajectory recognition, aiming at the problems of existing a lot of noise for handwriting trajectories, and difficult to segment multiple continuous hand gestures accurately, a long short‐term memory‐based dynamic probability (DP‐LSTM) method is proposed. Firstly, obtain the classification result for each sub‐period in the whole time period by using LSTM; secondly, cluster the classification results by non‐maximum suppression for trajectory algorithm to eliminate interference of invalid subsets; Finally, the end point of the valid trajectory is obtained according to the characteristics of the probability change, thus realising dynamic trajectory segmentation and recognition. In order to evaluate the performance of the DP‐LSTM, this method is evaluated by using an Arabic numerals gesture database. The experiments show that the DP‐LSTM has a high recognition rate for continuous hand gestures and can recognise its in real time.