# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
## 4 Results  
In this section, we present quantitative experimentation with the methods previously introduced in this work. In Table 1, we can observe the evaluation of the different proposed approaches and their reduction rates. By analyzing the results, we can observe that the method labeled _Features_ is the best concerning reduction and metrics, even outperforming the original metrics obtained by the baseline.  
Figure 3: Overview of single-frame methods. The initial dimensionality reduction step involves selecting either the central frame or the mean pixel values of the frames. Subsequently, visual embeddings are obtained using 2D CNNs, Vision Transformers, or DinoV2. Finally, classification is performed using an MLP. Discontinued lines indicate that only one of the models and frames are selected for use in each experiment.  
The experiments demonstrate that the most relevant features were obtained by extracting embeddings from large pre-trained video models. These embeddings effectively capture high-level information from prior tasks, reducing the representation size by more than 6000 times while improving performance across various metrics. While this method is highly efficient for training, its benefits are more limited during the inference time due to the time required to extract the embeddings from the pre-trained models. The effectiveness of this method can be graphically observed in Figure 4.  
The second-best method involves removing temporal information by processing only a single frame from each video sequence. We tested two strategies for selecting this single frame: The first was picking the central frame and calculating the mean frame pixel-wise across the entire sequence. The results indicate that using the mean frame leads to slightly better performance than using the central frame, likely because it retains some contextual information from the entire sequence. Features are extracted from the single frame using pre-trained image models, being a more lightweight process than the previous method in the video processing stage. As such, this could be the most suitable option in resource-constrained environments. Despite removing temporal information, this method achieves highly competitive performance metrics.  
Among the methods we proposed, the one based on Autoencoders performed the worst. Its lack of effectiveness can be attributed to overfitting, which occurred due to the limited availability of large datasets for this particular task. Figure 5 shows the learning curves for the loss functions for the different proposed methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Reduction** \\ \hline Baseline & 0.558 & 0.335 & **0.438** & 0.371 & - \\ \hline Features & **0.607\({}^{\pm 0.004}\)** & 0.423\({}^{\pm 0.02}\) & 0.424\({}^{\pm 0.009}\) & 0.398\({}^{\pm 0.05}\) & **6021** \\ Autoencoder & 0.513\({}^{\pm 0.002}\) & 0.279\({}^{\pm 0.25}\) & 0.170\({}^{\pm 0.001}\) & 0.144\({}^{\pm 0.003}\) & 235 \\ Single Frame (mean) & 0.576\({}^{\pm 0.004}\) & **0.442\({}^{\pm 0.01}\)** & 0.419\({}^{\pm 0.01}\) & **0.409\({}^{\pm 0.004}\)** & 1176 \\ Single Frame (central) & 0.556\({}^{\pm 0.007}\) & 0.385\({}^{\pm 0.006}\) & 0.390\({}^{\pm 0.01}\) & 0.368\({}^{\pm 0.004}\) & 3136 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of the proposed dimensionality reduction techniques, including their performance metrics and the reduction achieved relative to the original input size. Evaluations were conducted 5 times to mitigate the effects of randomness. The symbol \(\pm\) indicates the 95% confidence interval.  
Figure 4: Confusion matrix comparing the baseline approach with the proposed best dimensionality reduction method on the Visual WetlandBirds Dataset test set.