Real-Time Object Detection and Tracking for Search and Rescue Operations

Abstract

Real time object detection and tracking are of significant importance in improving search and rescue (SAR) by offering the ability to quickly and efficiently identify goals in adverse environments. These systems build on developments in computer vision and deep learning and are comprised of superordinate algorithms such as CNNs and transformers to process visual data from drones or cameras and other external sensors. This element guarantees the fast localization of objects such as humans, debris, or vehicles and constant tracking of those in motion, even at crowded scenes. Some of the main issues treated involve low illumination environments; occlusions; and, the practical requirements of real-time implementations compatible with the power-constrained environment of edge hardware. With the help of certain methodologies used in these systems such as multi-modal sensor fusion and adaptive tracking the chances of success for SAR are enhanced simply because these systems sharpen the techniques used in the search and rescue missions besides minimizing the response time. To this end, the methodologies, tools, and the possible use of real-time object detection and tracking technologies for SAR contexts are discussed in this paper with an aim of demonstrating how they enhance the desirable changes to disaster response operations.