Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4092
Title: | MediaPipe with LSTM Architecture for Real-Time Hand Gesture Recognization |
Authors: | Biswas, Sougatamoy Nandy, Anup Naskar, Asim Kumar Saw, Rahul |
Keywords: | Gesture recognition LSTM MediaPipe Human-computer interaction |
Issue Date: | Nov-2023 |
Citation: | 8th International Conference On Computer Vision and Image Processing (CVIP) 2023 At IIT Jammu During 3rd -5th November 2023 |
Abstract: | Gesture recognition plays a vital role in the area of research for human-computer interaction (HCI). The integration of MediaPipe with Long Short Term Memory (LSTM) architecture holds tremendous potential for real-time hand gesture recognition. MediaPipe provides a robust and versatile framework for capturing and processing multimedia input, such as video streams from cameras or pre-recorded video files. The temporal modeling capabilities of LSTM captures the temporal dynamics of hand gestures. This research paper aims to present a novel method utilizing the MediaPipe with LSTM architecture for real-time hand gesture recognition. A test on real-time gesture recognition is performed to evaluate the performance of the suggested model. Our results demonstrate that the suggested method outperforms other state-of-theart approaches on our custom made dataset with an accuracy of 98.99%. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/4092 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_CVIP_SBiswas_MediaPipe.pdf | 510.74 kB | Adobe PDF | View/Open Request a copy |
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