mlsanityData to 7 Oct 2026

TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs

arXiv 2210.03270 ↗First posted 7 Oct 2022cs.RO cs.CVcited by 0

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Abstract (arXiv)

We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera. Ultimately TRADE enables 3d-aware target following. Tracking-by-detection approaches are vulnerable to target switching, especially between similar objects. Thus, TRADE predicts and incorporates the target 3D trajectory to select the right target from the tracker's response map. Unlike static environments, depth estimation of a moving target from a single camera is a ill-posed problem. Therefore we propose a novel 3D localization method for ground targets on complex terrain. It reasons about scene geometry by combining ground plane segmentation, depth-from-motion and single-image depth estimation. The benefits of using TRADE are demonstrated as tracking robustness and depth accuracy on several dynamic scenes simulated in this work. Additionally, we demonstrate autonomous target following using a thermal camera by running TRADE on a quadcopter's board computer.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 7 Oct 2022.

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16 of 26 references.

References · 16

  1. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  2. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
  3. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  4. 2019Tracking without bells and whistlescited by 382
  5. 2021Learning Spatio-Temporal Transformer for Visual Trackingcited by 348
  6. 2019Learning Discriminative Model Prediction for Trackingcited by 344
  7. 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 277
  8. 2021Learning Target Candidate Association to Keep Track of What Not to Trackcited by 131
  9. 2020Know Your Surroundings: Exploiting Scene Information for Object Trackingcited by 119
  10. 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 78
  11. 2020Jointly Modeling Motion and Appearance Cues for Robust RGB-T Trackingcited by 47
  12. 2017Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularizationcited by 36
4 more
  1. 2020Dynamic SLAM: The Need For Speedcited by 24
  2. 2019Eye in the Sky: Drone-Based Object Tracking and 3D Localizationcited by 13
  3. 2021Range-Visual-Inertial Odometry: Scale Observability Without Excitationcited by 9
  4. 2018Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometrycited by 6

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