CCTag Project Overview
The CCTag library is an innovative tool designed for the detection of markers comprised of concentric circles. It facilitates implementations using either CPUs or GPUs, making it versatile for various hardware configurations. The core of the library is based on a study presented in a significant publication from the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Purpose and Functionality
CCTag is crafted for the precise detection and localization of circular fiducials, even under challenging conditions. These fiducials are essentially markers used in various computer vision applications. By leveraging the capability of both CPU and GPU architectures, CCTag ensures efficient processing of images featuring these markers.
Academic Foundation
The library’s development is grounded in academic research, specifically the work titled "Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions." This paper was authored by Lilian Calvet, Pierre Gurdjos, Carsten Griwodz, and Simone Gasparini. For those interested in exploring the foundational research, the paper is available through IEEE and can be cited using a provided BibTeX entry.
Marker Library
To utilize CCTag effectively, users can print markers available in the library's resources. It's crucial to respect the layout and margins provided to achieve the best detection and localization performance. Users are advised to avoid using materials that might bend, ensuring accuracy in applications.
Technical Requirements
The software requires either CUDA 8.0 or newer, or at least CUDA 7.0, with noted exceptions for CUDA 7.5, which may exhibit runtime errors on specific devices like the GTX980Ti. Additionally, the GPU device used must support a compute capability of at least 3.5. Compatibility information for graphics cards is available for users to verify their setup.
Building and Running
Users interested in building the library can refer to the installation guide found in the project's text files. The project supports continuous integration on both Windows and Linux platforms, with status badges available to reflect current build states. Once built, users can run detection tasks on sample images using the command-line interface provided.
Documentation and Licensing
Comprehensive documentation is available to guide users through installation, configuration, and usage, accessible via the Read the Docs page. The library is distributed under the MPL v2 license, which details the terms of use and distribution.
Authors and Contributions
Key contributors to the project include Lilian Calvet, Carsten Griwodz, Stian Vrba, Cyril Pichard, and Simone Gasparini, each bringing their expertise in either CPU or GPU implementations. The project received significant support from the European Union's Horizon 2020 programme through the POPART project, along with performance optimization contributions from the Norwegian RCN FORNY2020 project, FLEXCAM.
In summary, the CCTag library stands as a robust solution for detecting circular fiducials, built on a strong academic foundation and designed to operate efficiently on modern computing hardware.