ChromaTag: A Colored Marker and Fast Detection Algorithm

@article{DeGol2017ChromaTagAC,
  title={ChromaTag: A Colored Marker and Fast Detection Algorithm},
  author={Joseph DeGol and Timothy Bretl and Derek Hoiem},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1481-1490}
}
Current fiducial marker detection algorithms rely on marker IDs for false positive rejection. [] Key Result Our contribution is significant because fiducial markers are often used in real-time applications (e.g. marker assisted robot navigation) where heavy computation is required by other parts of the system.
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40 Citations
Highly Influential Citations
5
Background Citations
22
Methods Citations
8

40 Citations

Efficient and Robust Circle Grids for Fiducial Detection
TLDR
A new marker coding system design based on 3 × 3 grids of individual circles based on a novel way of composing individual fiducial tags into larger grids that enables the use of larger markers without adapting the detection pipeline, resulting in additional occlusion resistance and error detection.
LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
TLDR
LiDARTag, a novel fiducial tag design and detection algorithm suitable for light detection and ranging (LiDAR) point clouds, is introduced and it is confirmed that the proposed method can reliably provide a tag's pose and unique ID code.
Improved Structure from Motion Using Fiducial Marker Matching
In this paper, we present an incremental structure from motion (SfM) algorithm that significantly outperforms existing algorithms when fiducial markers are present in the scene, and that matches the…
ArUcoRSV: Robot Localisation Using Artificial Marker
TLDR
ArUcoRSV applies an automated perspective using marker detection, refining rejected marker and pose estimation, and produces accurate marker detection for robot identification and achieves significant improvements in detection rate with high positional accuracy.
DeepTag: A General Framework for Fiducial Marker Design and Detection
TLDR
A general deep learning based framework, DeepTag, for ducial marker design and detection and an effective procedure to synthesize training data on the y without manual annotations is proposed, which can easily adapt to existing and newly-designed marker families.
TopoTag: A Robust and Scalable Topological Fiducial Marker System
TLDR
Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy.
An efficient, dense and long-range marker system for the guidance of the visually impaired
TLDR
The experiments show that the proposed bundle marker-vision algorithm outperforms the alternatives in terms of distance and angle and also that it is very robust to changes in lighting conditions, thus making it a good intelligent system for guiding people with visual impairments in their day to day use of public transportation systems.
DeepFormableTag: end-to-end generation and recognition of deformable fiducial markers
TLDR
This paper presents the captured results of 3D scanning using a 2D array of multiple fiducial markers as structured light patterns as well as motion tracking of human body motion using the authors' deformable markers.
Design and Implementation of a Pose Estimation System Based on Visual Fiducial Features and Multiple Cameras
TLDR
A pose estimation system based on visual fiducial features with multi-cameras is proposed to provide ground truth data for mobile robot indoor localization experiments and the validation result of 6 cm accuracy is achieved.
Fiducial marker-based Metric Initialization for Monocular SLAM
TLDR
The experimental results show that feature-based monocular SLAM can acquire metric scale and the world origin can be coordinated to the marker plane after initialization, and the scale outputs can reach centimeter accuracy with real-time performance in a normal computing unit.
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