Memory Maps for Video Object Detection and Tracking on UAVs
This work addresses the problem of robust and interpretable object tracking for UAV applications, representing an incremental advancement by leveraging metadata.
The paper tackles video object detection and tracking on UAVs by using metadata to create a memory map of object locations in real-world coordinates, which boosts confidences and improves performance across multiple temporal computer vision tasks.
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.