# Edge-Aware Thermal Infrared UAV Swarm Tracking

arXiv: https://arxiv.org/abs/2607.12544 · HTML: https://mlsanity.com/paper/2607.12544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 23 of 23)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [DepGraph: Towards Any Structural Pruning](https://mlsanity.com/paper/2301.12900/) (2023)
- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models](https://mlsanity.com/paper/2606.03748/) (2026)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [Isomorphic Pruning for Vision Models](https://mlsanity.com/paper/2407.04616/) (2024)
- [Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals](https://mlsanity.com/paper/2304.07827/) (2023)
- [U2UData: A Large-scale Cooperative Perception Dataset for Swarm UAVs Autonomous Flight](https://mlsanity.com/paper/2408.00606/) (2024)
- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter](https://mlsanity.com/paper/2309.03058/) (2023)
- [GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering](https://mlsanity.com/paper/2311.16602/) (2023)
- [Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation](https://mlsanity.com/paper/2309.07016/) (2023)
- [Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID](https://mlsanity.com/paper/2503.17237/) (2025)
- [VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence](https://mlsanity.com/paper/2206.08669/) (2022)
- [U2UData+: A Scalable Swarm UAVs Autonomous Flight Dataset for Embodied Long-horizon Tasks](https://mlsanity.com/paper/2509.00055/) (2025)
- [YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion](https://mlsanity.com/paper/2503.07115/) (2025)
- [UAVDB: Point-Guided Masks for UAV Detection and Segmentation](https://mlsanity.com/paper/2409.06490/) (2024)

## Abstract (arXiv)

Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for edge devices, yet its constant-velocity assumption often breaks down under highly dynamic UAV motion and thermal sensor jitter. More sophisticated nonlinear estimators can improve robustness but often introduce additional computational costs. To address this gap, we propose an edge-aware online tracking pipeline centered on the Adaptive Kinematic Kalman Filter (AKKF), which augments the linear KF with state-dependent kinematic modeling while preserving real-time efficiency. Combined with transient false-positive suppression and kinematics-driven predictive coasting, the presented pipeline improves trajectory continuity under challenging TIR conditions. Experiments on the Beyond Strong Baseline (BSB) benchmark provide a starting point for edge-aware UAV tracking by jointly evaluating tracking performance and computational efficiency, offering insights toward future real-time deployment.