# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 6 Conclusions  
We explored various methods to reduce the dimensionality of videos from the Visual WetlandBirds Dataset for bird action classification. Among the approaches we proposed, the most effective in terms of accuracy and dimensionality reduction utilizes a feature extractor based on a Swing Transformer pre-trained on the Kinetics 400 dataset. This method outperformed the current state-of-the-art techniques while achieving a remarkable reduction in the classifier input size of over 6000 times. As a result, it significantly decreased both the training time and the computational resources required.  
While our study was specifically tailored to the Visual WetlandBirds Dataset, the principles we introduced can be applied to other datasets featuring similar challenges--namely, fast movements in noisy natural environments. This flexibility makes our approach well suited for a wide range of animal action videos captured in real-world settings. Consequently, our work opens up promising avenues for future research, especially as new comparable datasets emerge and bring forth fresh challenges in action detection and classification.  
Although we conducted an exhaustive ablation study of the methods we tested, several topics for future work remain open. While we implemented various dimensionality reduction techniques, there are other traditional methods we have yet to explore. Additionally, combining the reduced representations we proposed could further enhance the classifiers' performance. Finally, even though it is beyond the scope of this paper, applying these reduced representations to the entire pipeline of action detection and classification could offer valuable insights into the quality of the reduction techniques that we proposed.  
Figure 11: Impact rate on the performance of each proposed method.  
Figure 10: Impact of class weight on the performance of each proposed method.  
Figure 9: Impact of batch size on the performance of each proposed method.  
**Author Contributions:** Conceptualization, P.R.-P. and D.O.-P; methodology, P.R.-P., D.O.-P. and J.G.-R.; software, P.R.-P., D.O.-P., D.M.-P., M.B.-L. and J.R.-J.; validation, P.R.-P., D.O.-P., D.M.-P., M.B.-L., H.H.-L. and A.I.; formal analysis, P.R.-P., D.O.-P., D.M.-P. and M.B.-L.; investigation, P.R.-P., D.O.-P. and J.G.-R.; resources, J.G.-R., S.K., D.V. and D.N.; data curation, P.R.-P., D.O.-P., H.H.-L. and A.I.; writing--original draft preparation, P.R.-P., D.O.-P., D.M.-P., M.B.-L. and J.R.-J.; writing--review and editing, P.R.-P., D.O.-P., D.M.-P., M.B.-L., J.R.-J., H.H.-L., A.I., J.G.-R., S.K., D.V. and D.N.; validation, P.R.-P., D.O.-P., D.M.-P. and M.B.-L.; visualization, [PERSON], [PERSON], [PERSON], [PERSON] and A.I.; supervision, [PERSON], S.K., D.V. and D.N.; project administration, [PERSON], S.K., D.V. and D.N.; funding acquisition, [PERSON], S.K., D.V. and D.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We would like to thank \"A way of making Europe\" European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the \"CHAN-TWIN\" project (grant TED2021-130890B-C21. HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networkKs And distriButed federated Learning). This work is part of the HELEADE project (TSI-100121-2024-24), funded by the Spanish Ministry of Digital Processing and by the European Union NextGeneration EU. This work was also supported by three Spanish national and two regional grants for PhD studies, FPU21/00414, FPU22/04200, FPU23/00532, CIACIF/2021/430 and CIACIF/2022/175.  
**Data Availability Statement:** The dataset used during this study is publicly available at [[https://doi.org/10.5281/zenodo.14355257](https://doi.org/10.5281/zenodo.14355257)]([https://doi.org/10.5281/zenodo.14355257](https://doi.org/10.5281/zenodo.14355257)) (accessed on 15 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.