Memory Maps for Video Object Detection and Tracking on UAVs

Kiefer, Benjamin, Quan, Yitong, Zell, Andreas

arXiv.org Artificial Intelligence 

Abstract-- This paper introduces a novel approach to video object detection detection and tracking on Unmanned Aerial Vehicles (UAVs). By incorporating metadata, the proposed approach creates a memory map of object locations in actual world coordinates, providing a more robust and interpretable representation of object locations in both, image space and the real world. We use this representation to boost confidences, resulting in improved performance for several temporal computer vision tasks, such as video object detection, short and long-term single and multi-object tracking, and video anomaly detection. These findings confirm the benefits of metadata in enhancing the capabilities of UAVs in the field of temporal computer vision and pave the way for further advancements in this area. This internal understanding of the surrounding over time in a geometrically sensible way, resulting in more geometry allows us to reason robustly about the existence and robust predictions.

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