# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 2 Materials and Methods
### Image Processing
The service is also valuable in authentication and security contexts, enabling tasks such as face comparison for secure zone access or visual surveillance to inspect photos for objects or people of interest or concern [27]. The comprehensive set of features provided by Amazon Rekognition makes it a versatile tool for developers and businesses seeking advanced image and video analysis capabilities in the cloud. The primary objective of leveraging Amazon Rekognition is to automatically identify and locate objects within the aerial imagery captured during automated drone flights. Thisencompasses a broad range of objects, such as vehicles, individuals, and other relevant items based on the specific application scenario. The real-time processing capabilities of Amazon Rekognition are paramount, allowing for swift analysis of images and facilitating rapid decision making in dynamic surveillance scenarios. Integral to the success of this research is the continuous evolution of Amazon Rekognition's deep learning models. The system benefits from the latest advancements in object detection research, ensuring that the image processing phase remains at the forefront of technological innovation. Moreover, the image processing methodology adopted in this research extends beyond basic object detection. It incorporates insights from recent advancements in deep learning models, transfer learning techniques, and optimization strategies. This meticulous approach ensures that the image processing algorithms are not only accurate but also adaptive to varying environmental conditions and scenarios encountered during aerial surveillance. The culmination of this research effort includes the development of an Android application named \"Recognize,\" implemented using Kotlin and JAVA within Android Studio. Positioned subsequent to the DT Drones and Dronelink phases, Recognize plays a crucial role in the experimental framework. Upon deploying a specified fleet of drones to capture images of a common target, the application seamlessly integrates the Amazon Rekognition API, specifically utilizing Amazon Rekognition Custom Labels. Users input the number of drones utilized and are prompted to upload images captured by these drones. The application orchestrates a sophisticated analysis of these images through Amazon Rekognition Custom Labels, facilitating the detection of objects with a customized focus. In the final phase, Recognize presents detailed results, showcasing confidence levels associated with each detected object. The confidence levels are calculated using the confidence interval of the proportion method, ensuring a statistically rigorous evaluation of object detection accuracy (Appendix B). This application represents a significant stride in the integration of cutting-edge technology, contributing to the advancement of aerial surveillance and image processing paradigms. The application interface and screens are shown in Figure 2(a) below. The above Figure 2(b) illustrates the Recognize application architecture. By integrating the \"Recognize\" application into the workflow, this research makes a practical application of cutting-edge technology within the domain of aerial surveillance. The comprehensive approach, combining automated drone flights, precise location control through Dronelink, and advanced image analysis with the \"Recognize\" application, the research constructs a highly effective and evolving surveillance system. This integration measures the accuracy of the Delaunay triangulation approach, enabling timely responses and interventions based on the analyzed results. Overall, the research marks a significant advancement in the field of aerial surveillance and reconnaissance through the seamless integration of cutting-edge technologies.