DABACO: A Multi-Camera Dataset and Benchmark for Screen Localization and Pointing Estimation

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.