Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

Sensors

Alberto Tellaeche Iglesias¹  Juan Ignacio Vázquez Gómez¹  Surajit Saikia²  Ignacio Fidalgo Astorquia² 

[1] Computer Science, Electronics and Communication Technologies Department, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;DeustoTech-Deusto Institute of Technology, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain;

关键词: real time;    deep learning;    gesture detection;    embedded systems;   

DOI  :  10.3390/s21248202

来源: DOAJ

【 摘 要 】

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

【 授权许可】

Unknown