# Optimizing IoT Video Data: Dimensionality Reduction for Efficient Deep Learning on Edge Computing
###### Abstract  
The rapid loss of biodiversity significantly impacts birds' environments and behaviors, highlighting the importance of analyzing bird behavior for ecological insights. With the growing adoption of Machine Learning (ML) algorithms in the Internet of Things (IoT) domain, edge computing has become essential to ensure data privacy and enable real-time predictions by processing high-dimensional data, such as video streams, efficiently. This paper introduces a set of dimensionality reduction techniques tailored for video sequences based on cutting-edge methods for this data representation. These methods drastically compress video data, reducing bandwidth and storage requirements while enabling the creation of compact ML models with faster inference speeds. Comprehensive experiments on bird behavior classification in rural environments demonstrate the effectiveness of the proposed techniques. The experiments incorporate state-of-the-art deep learning techniques, including pre-trained video vision models, Autoencoders, and single-frame feature extraction. These methods demonstrated superior performance to the baseline, achieving up to a 6000-fold reduction in data size while reaching a classification accuracy of 60.7% on the Visual WetlandBirds Dataset and obtaining state-of-the-art performance on this dataset. These findings underline the potential of using dimensionality reduction to enhance the scalability and efficiency of bird behavior analysis. d 20251753 2025175  
subtle changes in their habitats, making them indispensable in monitoring and conservation efforts [5]. Video classification can be used for automating the study of bird behavior. However, this task can be resource-intensive, requiring the analysis of both visual and temporal data, which presents significant challenges in terms of time and effort. Additionally, it presents several challenges inherent to the task itself. The main one is the high dimensionality of the data the models have to process. This leads to a sparse data distribution due to the curse of dimensionality [6], requiring lots of samples to train a classifier without significant overfitting. Moreover, the high dimensionality significantly increases the number of trainable parameters in the classifiers, which in turn demands greater computational resources for both training and inference. In the context of bird behavior recognition, resources are often highly constrained due to deployment in rural environments. To address these challenges, we present a study focused on reducing the dimensionality of video recordings by transforming them into smaller and more compact representations while preserving the most relevant features for classification. To achieve this, we employ various state-of-the-art deep learning techniques, including the employment of embeddings obtained from large video vision models, such as 3D Convolutional Neural Networks (3 DCNNs) [7; 8] or Video Transformers [9; 10]. In addition, other techniques are proposed, including Autoencoders and extracting visual features from a single frame within the sequence. We present a comprehensive ablation study to identify the most effective methods and parameters. Our results demonstrate that the proposed methodologies can reduce video representations by a factor of 6000, while these compact representations outperform the baseline model. All experiments were carried out on the Visual WetlandBirds Dataset [11], yielding up to a 60.7% accuracy increase over the test set and presenting new state-of-the-art performance. Furthermore, they significantly reduce training time due to the smaller data size. In summary, our key contributions are as follows:  
* We proposed three different approaches to dimensionality reduction for the specific task of action classification from videos of birds in natural environments. These methods can reduce the size of a video by up to 6000 times for much faster training and inference.