Tracking fiducial markers with discriminative correlation filters

In the last few years, squared fiducial markers have become a popular and efficient tool to solve monocular local-ization and tracking problems at a very low cost. Nevertheless, marker detection is affected by noise and blur: small camera movements may cause image blurriness that prevents marker detection. The contribution of this paper is two-fold. First, it proposes a novel approach for estimating the location of markers in images using a set of Discriminative Correlation Filters (DCF). The proposed method outperforms state-of-the-art methods for marker detection and standard DCFs in terms of speed, precision, and sensitivity. Our method is robust to blur and scales very well with image resolution, obtaining more than 200fps in HD im-ages using a single CPU thread. As a second contribution, this paper proposes a method for camera localization with marker maps employing a predictive approach to detect visible markers with high precision, speed, and robustness to blurriness. The method has been compared to the state-of-the-art SLAM methods obtaining, better accuracy, sensitivity, and speed. The proposed approach is publicly available as part of the ArUco library. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

Squared fiducial markers have become popular in solving monocular localization and tracking problems, and the proposed method using Discriminative Correlation Filters shows superior performance in marker detection with robustness to blur. Furthermore, a predictive approach is employed for camera localization with marker maps, achieving high precision, speed, and robustness.