DISSERTATION

Gesture Recognition using Neural Networks

Abstract

The advances in technology have brought in a lot of changes in the way humans go about their lives. This has enhanced the significance of Artificial Neural Networks and Computer Vision- based interactions with the world. Gesture Recognition is one of the major focus areas in Computer Vision. This involves Human Computer Interfaces (HCI) that would capture and understand human actions. In this project, we will explore how Neural Network concepts can be applied in this challenging field of Computer Vision. By leveraging the latest research for Gesture Recognition, we researched on how to capture the movement across different frames of the gestures in videos. We experimented on preprocessed 2D and 3D data by applying various Neural Network models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) using Time Series Classification Technique to recognize the Gesture.

Keywords:
Gesture Computer science Convolutional neural network Gesture recognition Artificial intelligence Artificial neural network Focus (optics) Sketch recognition Field (mathematics) Deep learning Computer vision Human–computer interaction

Metrics

6
Cited By
0.00
FWCI (Field Weighted Citation Impact)
16
Refs
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Hand Gesture Recognition Systems
Physical Sciences →  Computer Science →  Human-Computer Interaction
Human Pose and Action Recognition
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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