Camera-RFID Fusion for Robust Asset Tracking in Forested Environments

TL;DR

This paper presents a novel camera-RFID fusion system that combines stereo vision and RFID data to improve asset tracking accuracy in forested environments, overcoming limitations of each modality.

Contribution

It introduces a new fusion framework with advanced trajectory-matching algorithms, achieving centimeter-level accuracy in natural forested settings.

Findings

Achieved reliable tag localization despite occlusions and temporary loss of visual contact.

Bridged the accuracy gap from meter-level RFID to centimeter-level vision.

First application of camera-RFID fusion for asset tracking in forests.

Abstract

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.