发表机构
Universidad de La Laguna (ULL)（拉古纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低成本屏幕定位与指向估计缺乏真实视频数据的问题，本文提出DABACO多相机数据集及基准，包含嵌入式平台采集的视频、系统标注流程和开源评估工具，提供两个参考基线以支持算法开发与评估。
AI 中文摘要
屏幕定位和指向估计是低成本交互设备的关键技术。然而，开发和评估这些算法需要真实的数据：合成采集无法完全再现物理采集过程中的光学畸变、卷帘快门、运动模糊和显示处理，且大多数现有数据集提供的是静态图像，而非评估连续指向所需的视频。本文介绍了DABACO数据集。该数据集在DABACO（Dispositivo Apuntador de BAjo COste，即低成本指向设备）项目框架内开发，支持嵌入式系统上屏幕检测和基于相机的指向算法的开发与评估。数据集包含由多个低成本相机传感器捕获的视频序列，包括单色全局快门和彩色卷帘快门模块，运行于树莓派4B和ESP32-S3等嵌入式平台。我们提出了一种系统化的标注流程，结合临时视觉水印、光流跟踪、人工验证和标记去除。原始带标记的采集图像和无标记图像，连同明确的角点标注和修改掩码一起发布，以支持审计和潜在重建偏差的研究。此外，我们发布了一个开源评估工具包，包含两个参考基线：一个基于边缘和轮廓几何的经典屏幕检测流程，以及一个无需数据集特定训练即可评估的闭词汇现成YOLOv8分割模型。两者均在通用计算机上使用交并比、角点误差和指向误差进行评估，为未来的嵌入式实现建立了初步参考结果。总体而言，DABACO填补了现有资源的空白，旨在支持新型低成本屏幕定位和指向系统的开发与评估。
英文摘要
Screen localization and pointing estimation are key to low-cost interactive devices. Yet developing and evaluating these algorithms requires realistic data: synthetic captures cannot fully reproduce the optical distortion, rolling shutter, motion blur, and display processing of a physical acquisition, and most existing datasets provide static images rather than the video needed to assess continuous pointing. In this paper, we introduce the DABACO Dataset. Developed within the DABACO (Dispositivo Apuntador de BAjo COste, or Low-Cost Pointing Device) project, this dataset supports the development and evaluation of screen detection and camera-based pointing algorithms for embedded systems. It comprises video sequences captured with multiple low-cost camera sensors, including monochrome global-shutter and color rolling-shutter modules, on embedded platforms such as the Raspberry Pi 4B and ESP32-S3. We present a systematic annotation pipeline combining temporary visual watermarking, optical-flow tracking, manual verification, and marker removal. Both the original marked captures and the marker-free images, together with explicit corner annotations and modification masks, are released to support auditing and the study of potential reconstruction bias. In addition, we release an open-source evaluation toolkit with two reference baselines: a classical screen-detection pipeline based on edge and contour geometry, and a closed-vocabulary, off-the-shelf YOLOv8 segmentation model evaluated without dataset-specific training. Both are evaluated on a general-purpose computer using Intersection over Union, corner error, and pointing error, establishing initial reference results for future embedded implementations. Overall, DABACO addresses a gap in existing resources and is intended to support the development and evaluation of new low-cost screen-localization and pointing systems.
Comments21 pages, 5 figures, 6 tables. Dataset: https://doi.org/10.5281/zenodo.22797835. Code and toolkit: https://domondo.github.io/dabaco-dataset-toolkit/
