# Memory Maps for Video Object Detection and Tracking on UAVs

Source: [Memory Maps for Video Object Detection and Tracking on UAVs](<https://www.sota2.com/research/paper/68fa6eb71e209131cea41ba2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:04:50.062Z

Authors: Benjamin Kiefer, Yitong Quan, Andreas Zell

Published: 2023-03-06T21:29:45.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.03508>)

[PDF](<https://arxiv.org/pdf/2303.03508.pdf>)