# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 6 Conclusions  
In this paper, a new approach for human activity recognition task is presented. It consists of the application of CSP (normally used in EEG systems) as feature extraction method before performing the classification. The resolution of the used videos is low (images of 25 \(\times\) 25) in order to complete different tests. After getting the results, HOF features are extracted and new models are created to compare them to the results obtained by the CSP method.  
As previously mentioned, our approach gets good results taking into account the resolution of the images. Furthermore, after performing the statistical test, it has been shown that, at least for one of the methods, CSP features are better than HOF features.  
As future work other databases could be used, or the extra information provided by HMDB51 data-set could be taken into account, since apart from the action label, other meta-labels are also indicated in each clip. This information could be useful to make some preprocessing before the feature extraction method. Besides, the resolution of the images could be improved if more computational capacity was obtained, doing tests with different sizes of images (25 \(\times\) 25, 40 \(\times\) 40, 60 \(\times\) 60,...). In our approach three different classifiers have been used, but in the future more classifiers or even deep learning techniques could be applied after doing the feature extraction with the CSP method.  
In conclusion, it is shown that with a simple method normally used for other tasks acceptable results can be obtained, without having to use very complicated ideas to achieve our goals.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **HOF-KNN** & **HOF-LDA** & **HOF-RF** \\ \hline CSP3\_var-KNN & 1.00000 & 1.00000 & 0.10176 \\ CSP3\_var-LDA & 0.99995 & 1.00000 & 0.02410 \\ CSP3\_var-RF & 1.00000 & 1.00000 & 0.23000 \\ CSP5\_var-KNN & 1.00000 & 1.00000 & 0.08550 \\ CSP5\_var-LDA & 1.00000 & 1.00000 & 0.04669 \\ CSP5\_var-RF & 1.00000 & 0.99417 & 0.79919 \\ CSP10\_var-KNN & 0.99995 & 1.00000 & 0.02410 \\ CSP10\_var-LDA & 0.99980 & 1.00000 & 0.01651 \\ CSP10\_var-RF & 1.00000 & 0.99594 & 0.77079 \\ CSP3-KNN & 1.00000 & 0.99345 & 0.80825 \\ CSP3-LDA & 0.33847 & 0.01115 & 1.00000 \\ CSP3-RF & 0.23807 & 0.00584 & 1.00000 \\ CSP5-KNN & 1.00000 & 1.00000 & 0.19239 \\ CSP5-LDA & 0.32851 & 0.01053 & 1.00000 \\ CSP5-RF & 0.50285 & 0.02541 & 1.00000 \\ CSP10-KNN & 0.95234 & 1.00000 & **0.00104** \\ CSP10-LDA & 0.39047 & 0.01478 & 1.00000 \\ CSP10-RF & 0.25475 & 0.00659 & 1.00000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: [PERSON] post-hoc results.  
**Author Contributions:** Research concept and supervision of technical writing: B.S. and I.I.; Software implementation, concept development and technical writing: I.R.-M.; Results validation and supervision of technical writing: J.M.M.-O. and I.G.; Methodological analysis: I.R.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially funded by the Basque Government, Research Teams grant number IT900-16, ELKARTEK 3 KIA project KK-2020/00049, and the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), and the European Regional Development Fund (FEDER), grant number RTI2018-093337-B-II00 (MCIU/AEI/FEDER, UE). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.