# Eye in the Sky: Drone-Based Object Tracking and 3D Localization

arXiv: https://arxiv.org/abs/1910.08259 · HTML: https://mlsanity.com/paper/1910.08259/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2303.04378/) (2023)
- [Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond](https://mlsanity.com/paper/2405.14674/) (2024)
- [Ego3DT: Tracking Every 3D Object in Ego-centric Videos](https://mlsanity.com/paper/2410.08530/) (2024)
- [Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing](https://mlsanity.com/paper/2502.14992/) (2025)
- [Category-Level Global Camera Pose Estimation with Multi-Hypothesis Point Cloud Correspondences](https://mlsanity.com/paper/2209.14419/) (2022)
- [Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach](https://mlsanity.com/paper/2607.04352/) (2026)
- [A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones](https://mlsanity.com/paper/2307.09880/) (2023)
- [DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects](https://mlsanity.com/paper/2407.09051/) (2024)
- [Enhancing Nighttime UAV Tracking with Light Distribution Suppression](https://mlsanity.com/paper/2409.16631/) (2024)
- [TrackingMiM: Efficient Mamba-in-Mamba Serialization for Real-time UAV Object Tracking](https://mlsanity.com/paper/2507.01535/) (2025)
- [TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs](https://mlsanity.com/paper/2210.03270/) (2022)
- [Hard-Label Black-Box Attacks on 3D Point Clouds](https://mlsanity.com/paper/2412.00404/) (2024)

## Abstract (arXiv)

Drones, or general UAVs, equipped with a single camera have been widely deployed to a broad range of applications, such as aerial photography, fast goods delivery and most importantly, surveillance. Despite the great progress achieved in computer vision algorithms, these algorithms are not usually optimized for dealing with images or video sequences acquired by drones, due to various challenges such as occlusion, fast camera motion and pose variation. In this paper, a drone-based multi-object tracking and 3D localization scheme is proposed based on the deep learning based object detection. We first combine a multi-object tracking method called TrackletNet Tracker (TNT) which utilizes temporal and appearance information to track detected objects located on the ground for UAV applications. Then, we are also able to localize the tracked ground objects based on the group plane estimated from the Multi-View Stereo technique. The system deployed on the drone can not only detect and track the objects in a scene, but can also localize their 3D coordinates in meters with respect to the drone camera. The experiments have proved our tracker can reliably handle most of the detected objects captured by drones and achieve favorable 3D localization performance when compared with the state-of-the-art methods.