# Camera-RFID Fusion for Robust Asset Tracking in Forested Environments

arXiv: https://arxiv.org/abs/2604.26241 · HTML: https://mlsanity.com/paper/2604.26241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016)
- [Robotic Grasping of Fully-Occluded Objects using RF Perception](https://mlsanity.com/paper/2012.15436/) (2020)
- [Computing the Fr'echet Distance Between Uncertain Curves in One Dimension](https://mlsanity.com/paper/2105.09922/) (2021)
- [RF-Annotate: Automatic RF-Supervised Image Annotation of Common Objects in Context](https://mlsanity.com/paper/2211.08837/) (2022)
- [Tracking Wildfire Assets with Commodity RFID and Gaussian Process Modeling](https://mlsanity.com/paper/2512.15956/) (2025)

## Abstract (arXiv)

Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuation and multipath effects generally limit RFID spatial accuracy to the meter level. Conversely, while cameras employing stereo vision can achieve centimeter-level precision, relying solely on computer vision fails to resolve issues arising from spatial association ambiguity and partial occlusions in dense settings. Fusing these modalities allows systems to harness the high-accuracy benefits of vision while retaining the robust, non-line-of-sight identification advantages of RFID. Yet, a primary challenge in achieving this, which is the central focus of this paper, lies in accurately associating the disparate trajectories generated by these two sensors. To overcome this limitation, we introduce a novel camera--RFID fusion framework that integrates depth and object information with advanced trajectory-matching algorithms. By successfully bridging the meter-to-centimeter accuracy gap, the proposed approach helps achieve reliable tag localization even when assets temporarily leave the camera's field of view. To the best of our knowledge, this represents the first application of camera--RFID fusion for asset tracking in natural forested environments.