In this section
@article{Gionfrida:2022:10.3390/electronics11152427,
author = {Gionfrida, L and Rusli, W and Kedgley, A and Bharath, A},
doi = {10.3390/electronics11152427},
journal = {Electronics},
title = {A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition},
url = {http://dx.doi.org/10.3390/electronics11152427},
volume = {11},
year = {2022}
}
TY - JOUR
AB - This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.
AU - Gionfrida,L
AU - Rusli,W
AU - Kedgley,A
AU - Bharath,A
DO - 10.3390/electronics11152427
PY - 2022///
SN - 2079-9292
TI - A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition
T2 - Electronics
UR - http://dx.doi.org/10.3390/electronics11152427
UR - http://hdl.handle.net/10044/1/98865
VL - 11
ER -
Prof Anil Anthony Bharath
a.bharath@imperial.ac.uk
Telephone
+44 (0)20 7594 5463
Address
Room 4.12
Department of Bioengineering
Royal School of Mines Building
Imperial College London
