# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 2 Materials and Methods
### Image Processing
In essence, image processing lays the groundwork for image recognition by refining raw visual data and facilitating the extraction of meaningful features critical to the classification and interpretation of images by machine learning models. In the context of this research, the image processing phase is a critical component, serving as the analytical backbone for the results obtained through the combined use of Drones DT and Dronelink. The image processing application employed in this research relies on the Amazon Rekognition API, a sophisticated image and video analysis service powered by advanced deep learning algorithms. Amazon Rekognition is designed to excel in various image recognition tasks, including object and scene detection, and face analysis. Achieving a level of comprehension in computers akin to human understanding has long been a challenging task for computer scientists. Over past decades, various approaches have been explored to address this issue. The consensus that has emerged today is that deep learning, utilizing a combination of feature abstraction and neural networks, offers a powerful solution. While this approach can produce results that seem almost magical, it comes with a significant computational cost, especially during the intensive training phase. In the training phase, a deep learning network is presented with a diverse set of labeled examples to correlate features in images with specific labels, such as identifying a dog or a pet. This phase is computationally expensive due to the size and multi-layered nature of neural networks. Once trained, the network can efficiently evaluate new images against the learned features, expressing results with confidence levels (0 to 100%) rather than absolute facts, allowing for adaptable precision in various applications [25]. Amazon Rekognition, a fully-managed service powered by deep learning, has been meticulously developed by the computer vision team over many years. Analyzing billions of images daily, it has been trained on thousands of objects and scenes, making it available for use in various applications. Designed to run at scale, Rekognition comprehends scenes, objects, and faces, returning lists of labels for images and bounding boxes for faces, along with attributes. In a practical demonstration, Rekognition accurately labeled an image of a dog named [PERSON], recognizing it as an animal, a dog, a pet, and a golden retriever with high confidence. Notably, the labels are independent, reflecting the model's correlation of features without explicit understanding of relationships between labels [26]. Rekognition's capabilities extend to facial recognition, as demonstrated in an image of the author's wife and himself. The service identified faces, set up bounding boxes, and even detected emotions, noting that the author's wife appeared happy. Rekognition enables face comparison and recognition tasks, and its functionalities are accessible through a set of API functions. Rekognition's power is harnessed through API calls, allowing for programmable interaction. Functions like DetectLabels and DetectFaces enable users to replicate the demonstrated examples. The service also facilitates face indexing through functions like IndexFaces, extracting features known as face vectors for recognition. Moreover, Rekognition seamlessly processes images stored in Amazon S3 and can be integrated with AWS Lambda functions for efficient and scalable image processing. AWS Identity and Access Management (IAM) controls access to Rekognition APIs, ensuring secure usage. Rekognition finds diverse applications, particularly in scenarios where large photo collections need tagging and indexing. Its scalability allows for the processing of millions of photos daily without concerns about infrastructure setup. Visual search, tag-based browsing, and interactive discovery models become feasible with Rekognition.