# Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking

arXiv: https://arxiv.org/abs/2311.03561 · HTML: https://mlsanity.com/paper/2311.03561/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [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)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://mlsanity.com/paper/2304.05170/) (2023)
- [SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water](https://mlsanity.com/paper/2105.01922/) (2021)
- [Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2306.13074/) (2023)
- [1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results](https://mlsanity.com/paper/2211.13508/) (2022)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment](https://mlsanity.com/paper/2304.09471/) (2023)
- [Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information](https://mlsanity.com/paper/2301.07805/) (2023)
- [Memory Maps for Video Object Detection and Tracking on UAVs](https://mlsanity.com/paper/2303.03508/) (2023)
- [Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring](https://mlsanity.com/paper/2304.04816/) (2023)

## Built on by (most cited first, 4 of 4)

- [MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2403.10826/) (2024)
- [SCT-MOT: Enhancing Air-to-Air Multiple UAVs Tracking with Swarm-Coupled Motion and Trajectory Guidance](https://mlsanity.com/paper/2604.06883/) (2026)
- [Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking](https://mlsanity.com/paper/2505.18111/) (2025)
- [Multi-Modal Graph Neural Network-Inspired Vessel Identification and Tracking for UAV-Based Maritime Surveillance](https://mlsanity.com/paper/2610.13220/) (2026)

## Abstract (arXiv)

Re-identification (ReID) in multi-object tracking (MOT) for UAVs in maritime computer vision has been challenging for several reasons. More specifically, short-term re-identification (ReID) is difficult due to the nature of the characteristics of small targets and the sudden movement of the drone's gimbal. Long-term ReID suffers from the lack of useful appearance diversity. In response to these challenges, we present an adaptable motion-based MOT algorithm, called Metadata Guided MOT (MG-MOT). This algorithm effectively merges short-term tracking data into coherent long-term tracks, harnessing crucial metadata from UAVs, including GPS position, drone altitude, and camera orientations. Extensive experiments are conducted to validate the efficacy of our MOT algorithm. Utilizing the challenging SeaDroneSee tracking dataset, which encompasses the aforementioned scenarios, we achieve a much-improved performance in the latest edition of the UAV-based Maritime Object Tracking Challenge with a state-of-the-art HOTA of 69.5% and an IDF1 of 85.9% on the testing split.