scholarly journals Mobile terminal gesture recognition based on improved FAST corner detection

2019 ◽  
Vol 13 (6) ◽  
pp. 991-997 ◽  
Author(s):  
Chengfeng Jian ◽  
Xiaoyu Xiang ◽  
Meiyu Zhang
2022 ◽  
Vol 355 ◽  
pp. 03043
Author(s):  
Yushan Zhong ◽  
Yifan Jia ◽  
Liang Ma

In order to cultivate children’s imagination and creativity in the cognitive process, combined with the traditional hand shadow game, a children’s gesture education game based on AI gesture recognition technology is designed and developed. The game uses unity development platform, with children’s digital gesture recognition as the content, designs and implements the basic functions involved in the game, including AI gesture recognition function, character animation function, interface interaction function, AR photo taking function and question answering system function. The game is finally released on the mobile terminal. Players can recognize gestures through mobile cameras, interact with virtual cartoon characters in the game, watch cartoon character animation, understand popular science knowledge, and complete the answers in the game. The educational games can better assist children to learn digital gestures, enrich children’s ways of cognition, expand children’s imagination, and let children learn easily with happy educational games.


2016 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Seong Jeong ◽  
HongJun Ju ◽  
Hyo-Rim Choi ◽  
TaeYong Kim

2020 ◽  
Vol 79 (1) ◽  
pp. 47-57
Author(s):  
O. G. Viunytskyi ◽  
A. V. Totsky ◽  
Karen O. Egiazarian

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.


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