# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 9 Conclusions  
The rise of edge-based remote applications has created a demand for high-performance low-cost computing-based deep learning convolutional neural networks (DCNNs). The main idea of the DCNN is not new. However, optimization and expanding the applications of neural networks are now very demanding tasks due to the rise of edge computing and real-time response-based application deployment. Decoding hand gestures in real time requires a fast DCNN capable of interpreting variable-sized image inputs owing to the variation between cameras in modern systems. In this research, we proposed a new hand gesture classification system that can classify various hand gestures \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Original** & **Pruned** & **Mean** & **Spatial Pyramid** & **Mean** \\
**Samples** & **Generated** & **Classifier** & **Majority in** & **Classifier** & **Majority in Rows** \\
**in Each** & **Sequence** & **Predicted** & **Rows** & **Predicted** & **(Percent)** \\
**Class (fps)** & **Sequence** & **Sequence** & **(Percent)** & **Sequence** & **(Percent)** \\ \hline \multirow{3}{*}{5} & [H’ K’ J’ M] & [[PERSON] M] & 99.20 & [’H’ K’ J’ M] & 100 \\  & [Y’ G’ Z’ X] & [Y’ G’ Z’ X] & 99.20 & [’Y’ G’ Z’ X’] & 100 \\  & [T’ C’ W’ V’] & [T’ C’ W’ V’] & 99.20 & [’T’ C’ W’ V’] & 100 \\ \hline \multirow{3}{*}{10} & [[PERSON] K’ J’ M] & [[PERSON] K’ J’ M] & 99.23 & [[PERSON] M] & 100 \\  & [Y’ G’ Z’ X] & [Y’ G’ Z’ X] & 98.2 & [’Y’ G’ Z’ X’] & 100 \\  & [T’ C’ W’ V’] & [T’ C’ W’ V’] & 98.2 & [’T’ C’ W’ V’] & 99.97 \\ \hline \multirow{3}{*}{30} & [[PERSON] K’ J’ M] & [[PERSON] K’ J’ M] & 99.23 & [’H’ K’ J’ M] & 99.88 \\  & [Y’ G’ Z’ X] & [Y’ G’ Z’ X] & 98.99 & [’Y’ G’ Z’ X’] & 99.3 \\  & [T’ C’ W’ V’] & [T’ C’ W’ V’] & 98.99 & [’T’ C’ W’ V’] & 99.33 \\ \hline \multirow{3}{*}{60} & [[PERSON]] & [[PERSON]] & 98.99 & [’H’ [PERSON] M] & 99.33 \\  & [Y’ G’ Z’ X] & [Y’ G’ Z’ X] & 98.24 & [’Y’ G’ Z’ X’] & 99.67 \\ \cline{1-1}  & [T’ C’ W’ V’] & [T’ C’ W’ V’] & 98.56 & [’T’ C’ W’ V’] & 99.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Proposed sequencer performance analysis. with 94% accuracy. Moreover, we integrated a spatial pyramid pooling (SPP) layer into the proposed DCNN and used node pruning to make it less computationally resource intensive and image input dimension-invariant. Consequently, the proposed SPP-DCNN is the most reliable method of real-time gesture decoding. Moreover, we have introduced a novel algorithm for video-based gesture decoding that can process a video with any arbitrary input dimensions and variable frames per second (fps), which will decode an input gesture video into consecutive gesture classes. This proposed new and faster system can also be used as an advance ASL finger-spelling recogniser. The use of these compact SPP-DCNNs in various remote smart locations with minimal computing resources will ensure high performance with lower computing costs and better connectivity. Conceptualization, A.A. and J.K.; methodology, A.A.; software, A.A.; validation, J.P., A.A. and J.K.; formal analysis, A.A.; investigation, H.L. ; resources, K.K. ; data curation, A.A.; writing--original draft preparation, K.K. ; writing--review and editing, J.P. and H.-Y.K. ; visualization, K.K. ; supervision, H.L. ; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00314) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation) and also are results of a study on the supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government(MOTIE) (P0011931, The Establishment Project of Industry-University Fusion District). Finally, this project was also supported by the National Research Foundation of Korea (NRF) funded by the MSIT (NRF-2018R1A4A1025559).