Abstract
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.
Highlights
Gestures are key elements in the fields of interaction, understanding, and communication with machines
Final schema the Faster selected thefor different variations of the RCNN object detection algorithm in this work because their structure is based on an internal CNN network that can be created taking into account the computation capability available, making this approach fully adaptable to limited hardware
This article presents a research work to analyze the performance of the different RCNN approaches for the detection of hand gestures in real time, with the final objective of obtaining a lightweight system that fits in desktop PC, and can be run in IoT (Internet of Things) devices like, Google Coral or Nvidia Jetson platforms, providing a real-time response
Summary
Gestures are key elements in the fields of interaction, understanding, and communication with machines. 3. Final schema the Faster selected thefor different variations of the RCNN object detection algorithm in this work because their structure is based on an internal CNN network that can be created taking into account the computation capability available, making this approach fully adaptable to limited hardware. The main motivation of this work is to design an effective CNN architecture that can be deployed and used in computationally limited devices, offering real-time gesture detection capabilities in embedded devices with the previously cited limitation Following this motivation, this article presents a research work to analyze the performance of the different RCNN approaches for the detection of hand gestures in real time, with the final objective of obtaining a lightweight system that fits in desktop PC, and can be run in IoT (Internet of Things) devices like, Google Coral or Nvidia Jetson platforms, providing a real-time response.
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