Open AccessMobile terminal trajectory recognition based on improved LSTM model
Author(s) -
Jian Chengfeng,
Yang Meiling,
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.2019.0183
Subject(s) - softmax function , trajectory , computer science , feature (linguistics) , artificial intelligence , convergence (economics) , pattern recognition (psychology) , feature extraction , sequence (biology) , process (computing) , computer vision , algorithm , deep learning , philosophy , physics , linguistics , astronomy , economics , economic growth , operating system , biology , genetics
Mobile terminal hand trajectory recognition is challenged by spatio‐temporal variations, complex background, light variations as well as the limited computing resources for mobile devices. The authors propose a hand trajectory recognition method based on improved Long‐Short Term Memory network (LSTM) model. First, for the sake of eliminating the effects of complex background and light, the SSD hand detector and multi‐color space are combined to segment gesture. To eliminate the influence of trajectory points' overlap, the sparseness of the trajectory points is used to determine the start and end of the trajectory; then, to maintain the feature difference while decreasing the feature extraction time, they extract the trajectory feature sequence based on the central point direction angle; Finally, a new loss function (M‐Softmax loss) is proposed in LSTM training process to maximise the inter‐class variations and minimise intra‐class variations. At the same time, the adaptive training iteration number is proposed to ensure the convergence of the model and reduce the training time cost. The experimental results illustrate that the improved model learned features are more compact, and the model is easier to deal with external data changes. Authors’ proposed method has great significance in practical applications.