# Near-Shore Mapping for Detection and Tracking of Vessels

arXiv: https://arxiv.org/abs/2502.18368 · HTML: https://mlsanity.com/paper/2502.18368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data](https://mlsanity.com/paper/2105.08971/) (2021)
- [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://mlsanity.com/paper/2103.04316/) (2021)
- [Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation](https://mlsanity.com/paper/2201.04501/) (2022)
- [A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application](https://mlsanity.com/paper/1605.02406/) (2016)
- [Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation](https://mlsanity.com/paper/2307.08314/) (2023)
- [Dynamic Occupancy Grid Mapping with Recurrent Neural Networks](https://mlsanity.com/paper/2011.08659/) (2020)
- [Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data](https://mlsanity.com/paper/2104.03657/) (2021)
- [Robust Moving Objects Detection in Lidar Data Exploiting Visual Cues](https://mlsanity.com/paper/1609.09267/) (2016)
- [ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations](https://mlsanity.com/paper/2102.05869/) (2021)
- [Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps](https://mlsanity.com/paper/1812.08449/) (2018)
- [Multi-session Map Construction in Outdoor Dynamic Environment](https://mlsanity.com/paper/1807.08098/) (2018)

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

- [Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking](https://mlsanity.com/paper/2603.08457/) (2026)

## Abstract (arXiv)

For an autonomous surface vessel (ASV) to dock, it must track other vessels close to the docking area. Kayaks present a particular challenge due to their proximity to the dock and relatively small size. Maritime target tracking has typically employed land masking to filter out land and the dock. However, imprecise land masking makes it difficult to track close-to-dock objects. Our approach uses Light Detection And Ranging (LiDAR) data and maps the docking area offline. The precise 3D measurements allow for precise map creation. However, the mapping could result in static, yet potentially moving, objects being mapped. We detect and filter out potentially moving objects from the LiDAR data by utilizing image data. The visual vessel detection and segmentation method is a neural network that is trained on our labeled data. Close-to-shore tracking improves with an accurate map and is demonstrated on a recently gathered real-world dataset. The dataset contains multiple sequences of a kayak and a day cruiser moving close to the dock, in a collision path with an autonomous ferry prototype.