# Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos

arXiv: https://arxiv.org/abs/2508.01730 · HTML: https://mlsanity.com/paper/2508.01730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [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)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (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)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification](https://mlsanity.com/paper/2302.11813/) (2023)
- [Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking](https://mlsanity.com/paper/2308.00783/) (2023)
- [MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking](https://mlsanity.com/paper/2303.10404/) (2023)
- [SiamMOT: Siamese Multi-Object Tracking](https://mlsanity.com/paper/2105.11595/) (2021)
- [DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction](https://mlsanity.com/paper/2403.02075/) (2024)
- [Matching Anything by Segmenting Anything](https://mlsanity.com/paper/2406.04221/) (2024)
- [UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation](https://mlsanity.com/paper/2312.08952/) (2023)
- [SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking](https://mlsanity.com/paper/2211.08824/) (2022)
- [One More Check: Making "Fake Background" Be Tracked Again](https://mlsanity.com/paper/2104.09441/) (2021)
- [Towards Generalizable Multi-Object Tracking](https://mlsanity.com/paper/2406.00429/) (2024)
- [FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow](https://mlsanity.com/paper/2308.07207/) (2023)
- [Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach](https://mlsanity.com/paper/2408.00969/) (2024)
- [Uncertainty-aware Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2307.15409/) (2023)
- [DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking](https://mlsanity.com/paper/2403.02767/) (2024)
- [Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning](https://mlsanity.com/paper/2309.00942/) (2023)
- [Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation](https://mlsanity.com/paper/2411.12177/) (2024)
- [MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking](https://mlsanity.com/paper/2407.10485/) (2024)
- [DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects](https://mlsanity.com/paper/2407.09051/) (2024)
- [SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects](https://mlsanity.com/paper/2410.20079/) (2024)
- [STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking](https://mlsanity.com/paper/2409.11234/) (2024)

## Built on by (most cited first, 1 of 1)

- [UAV-OVVIS: Unmanned Aerial Vehicles Also Need Open-Vocabulary Video Instance Segmentation](https://mlsanity.com/paper/2607.08075/) (2026)

## Abstract (arXiv)

Multi-object tracking (MOT) aims to track multiple objects while maintaining consistent identities across frames of a given video. In unmanned aerial vehicle (UAV) recorded videos, frequent viewpoint changes and complex UAV-ground relative motion dynamics pose significant challenges, which often lead to unstable affinity measurement and ambiguous association. Existing methods typically model motion and appearance cues separately, overlooking their spatio-temporal interplay and resulting in suboptimal tracking performance. In this work, we propose AMOT, which jointly exploits appearance and motion cues through two key components: an Appearance-Motion Consistency (AMC) matrix and a Motion-aware Track Continuation (MTC) module. Specifically, the AMC matrix computes bi-directional spatial consistency under the guidance of appearance features, enabling more reliable and context-aware identity association. The MTC module complements AMC by reactivating unmatched tracks through appearance-guided predictions that align with Kalman-based predictions, thereby reducing broken trajectories caused by missed detections. Extensive experiments on three UAV benchmarks, including VisDrone2019, UAVDT, and VT-MOT-UAV, demonstrate that our AMOT outperforms current state-of-the-art methods and generalizes well in a plug-and-play and training-free manner.