Drones and Aerial Surveillance for Optical Flow Estimation in Adverse Outdoor Conditions

The modern world has seen a tremendous rise in the application of drones for a variety of surveillance and monitoring tasks, ranging from environmental monitoring to security operations. Drones, or unmanned aerial vehicles (UAVs), are extensively used due to their ability to capture aerial data efficiently, even from hard-to-reach places. However, adverse outdoor conditions like low light, rain, snow, and fog can severely affect their performance, particularly in capturing and analyzing motion via optical flow estimation. Optical flow, the pattern of apparent motion of image objects between two frames, is essential in helping drones navigate and track objects in real time. This chapter delves into the technical foundations of optical flow estimation, its relevance for drones in aerial surveillance, and the challenges that arise in adverse weather conditions. We further explore the role of deep learning techniques in enhancing optical flow under these conditions, along with case studies and real-world applications to highlight the robustness of modern solutions.