Abstract
The rapid advancement in drone/UAV technology has led to their extensive use in various domains including surveillance, agriculture, and environmental monitoring. Change detection in UAV videos is crucial for identifying significant alterations in the observed areas. However, challenges such as varying scales, resolutions, and orientations of video frames pose significant hurdles. This paper addresses these challenges by leveraging the SIFT algorithm for panoramic stitching and change detection, followed by the application of the YOLOv8 model for high-precision anomaly detection
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