Image-Domain Tilt Constrained Distributed Fusion for Maneuvering UAV Tracking with Multi-Camera Electro-Optical Observations

• URL: http://arxiv.org/abs/2607.01008v1
• Date: Wed, 01 Jul 2026 14:45:43 GMT
• Title: Image-Domain Tilt Constrained Distributed Fusion for Maneuvering UAV Tracking with Multi-Camera Electro-Optical Observations
• Abstract summary: This paper proposes an image-domain tilt constrained distributed fusion method for maneuvering UAV tracking.<n>The method uses the apparent roll and pitch of a rotorcraft target in the image as low-level maneuver cues.<n>The results show that image-domain tilt can provide useful acceleration constraints for robust short-horizon UAV prediction.
• Score: 1.5469452301122173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Short-horizon prediction is essential for electro-optical UAV tracking, especially when the target is small, maneuvering, or intermittently observed. Image center, line-of-sight, and range measurements provide direct constraints on target position, but their constraints on acceleration are weak. As a result, prediction can lag during aggressive maneuvers. This paper proposes an image-domain tilt constrained distributed fusion method for maneuvering UAV tracking. The method uses the apparent roll and pitch of a rotorcraft target in the image as low-level maneuver cues. A weak-prior auto-labeling pipeline first generates oriented bounding box and image-domain tilt labels from synchronized video, gimbal IMU, and UAV IMU data. A YOLO-OBB detector is then trained to provide online target position and tilt measurements. The front-end Python implementation is publicly available at github.com/ShineMinxing/PythonYOLO. In the fusion stage, the UAV state is modeled by position, velocity, and acceleration. Image-domain roll and pitch are introduced as acceleration-related pseudo-observations. For distributed tracking, one mobile gimbal camera and two fixed ground cameras are fused asynchronously. Camera attitude error states are augmented into the filter to absorb extrinsic drift and cross-camera systematic inconsistency. A Mahalanobis gate with time-since-last-valid covariance widening is used to reject false detections and handle dropouts. In simulation, adding roll/pitch observations reduces the prediction RMSE from 1.991 m to 0.821 m and decreases the cumulative prediction error by 60.75\%. In real distributed experiments, a self-consistency evaluation shows an 18.10\% reduction in cumulative prediction error. The results show that image-domain tilt can provide useful acceleration constraints for robust short-horizon UAV prediction.

Related papers

• Event-Based Motion Estimation via Oriented Distance Fields [55.23416502666468]
  Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion.<n>We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors.<n>On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods.
  arXiv  Detail & Related papers  (2026-08-25T08:27:42Z)
• FARI: Robust One-Step Inversion for Watermarking in Diffusion Models [89.29541056113442]
  Inversion-based watermarking is a promising approach to authenticate diffusion-generated images.<n>Existing approaches over-optimize internal truncation error.<n>textbfFARI is a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction.
  arXiv  Detail & Related papers  (2026-07-29T10:13:55Z)
• TACO: Trajectory Aligning Cross-view Optimisation [13.162763356411448]
  Cross-View Geo-localisation matches ground imagery against satellite tiles to give absolute position fixes.<n>Recent fine-grained CVGL methods regress sub-tile metric pose, but have only been evaluated as one-shot localisers.<n>We propose TACO, a tightly-coupled IMU + fine-grained CVGL pipeline that consumes a single reading at start-up and thereafter operates on onboard sensing alone.
  arXiv  Detail & Related papers  (2026-05-05T03:04:49Z)
• Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency [43.497514957094275]
  We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated data to regress the gate's pose.<n>We propose an unsupervised domain adaptation (UDA) approach using only real image sequences collected by the drone flying an arbitrary trajectory in front of a gate.
  arXiv  Detail & Related papers  (2026-03-03T12:46:25Z)
• Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering [0.5524043015533312]
  This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues.<n>Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework.
  arXiv  Detail & Related papers  (2026-03-02T15:48:51Z)
• SDG-Track: A Heterogeneous Observer-Follower Framework for High-Resolution UAV Tracking on Embedded Platforms [11.029096488950414]
  Real-time tracking of small unmanned aerial vehicles (UAVs) on edge devices faces a fundamental resolution-speed conflict.<n>We propose a Sparse Detection-Guided Tracker that adopts an Observer-Follower architecture to reconcile this conflict.<n>Experiments on a ground-to-air tracking station demonstrate that SDG-Track achieves 35.1 FPS system throughput while retaining 97.2% of the frame-by-frame detection precision.
  arXiv  Detail & Related papers  (2025-12-04T15:11:43Z)
• iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion [62.09575122593993]
  iGaussian is a two-stage feed-forward framework that achieves real-time camera pose estimation through direct 3D Gaussian inversion.<n> Experimental results on the NeRF Synthetic, Mip-NeRF 360, and T&T+DB datasets demonstrate a significant performance improvement over previous methods.
  arXiv  Detail & Related papers  (2025-11-18T05:22:22Z)
• SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos [50.51658520045165]
  Spike-d Optimal TrAnsport Saliency Region Detection (SOTA) is a framework that leverages the strengths of spike cameras while mitigating biases in both spatial and temporal dimensions.<n>Our method introduces Spike-based Micro-debias (SM) to capture subtle frame-to-frame variations.<n>SOTA refines predictions by reducing inconsistencies across diverse conditions.
  arXiv  Detail & Related papers  (2025-05-01T08:30:40Z)
• Unified Human Localization and Trajectory Prediction with Monocular Vision [64.19384064365431]
  MonoTransmotion is a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks.<n>We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs.
  arXiv  Detail & Related papers  (2025-03-05T14:18:39Z)
• RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark [61.987291551925516]
  We introduce the Unit Cycle Resolver, which incorporates a unit circle constraint loss to improve angle prediction accuracy.<n>Our approach can effectively improve the performance of existing state-of-the-art weakly supervised methods.<n>With the aid of UCR, we further annotate and introduce RSAR, the largest multi-class rotated SAR object detection dataset to date.
  arXiv  Detail & Related papers  (2025-01-08T11:41:47Z)
• Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring [71.60457491155451]
  Eliminating image blur produced by various kinds of motion has been a challenging problem. We propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative Filter. Our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-19T19:44:24Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• Lidar with Velocity: Motion Distortion Correction of Point Clouds from Oscillating Scanning Lidars [5.285472406047901]
  Lidar point cloud distortion from moving object is an important problem in autonomous driving. Gustafson-based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method.
  arXiv  Detail & Related papers  (2021-11-18T03:13:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.