Real World Engagement Attention Dataset #1c (RWEAD #1c)

RWEAD #1c is a group-behaviour video dataset acquired in an approximately 57 m² room (~10.8 m × ~7.0 m) of a university building hall, configured as an Indoor-category exhibition stand within a building hall. The sensing infrastructure comprises a single RGB camera (1920 × 1080 px, 30 fps) mounted at an elevated viewpoint, 3.5 m high and tilted 20° downward, overlooking the entire area. The positions of the camera and of the three Areas of Interest (AoIs) -- Desk, Maquette and TV -- were logged in a coordinate table. Because the hall has a glazed façade, people outside the area remain visible after exiting; an indoor event-area mask therefore separates the indoor area from the outside, and entries and exits are defined as crossings of the two door lines. The participant cohort consists of seven amateur actors (P#1–P#7) aged 20–30, all university students without formal acting training. Each participant received a script and was allotted five minutes for memorisation prior to data acquisition. Fifteen scripted recordings were acquired, five for each construct: conversion, retention, and commitment. The scripts include "positive" and "negative" cases, as well as group dynamics such as groups merging and splitting, members leaving and rejoining their group, exits and re-entries, overlapping groups, and occlusion by a structural pillar.

Authors

Institutions

Publication Details

Journal
Open Science Framework
Published
2026-10-27
DOI
https://doi.org/10.17605/osf.io/whnqv
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real World Engagement Attention Dataset #1c (RWEAD #1c)

João M. F. Rodrigues, Mohamed El Afia
2 links
Open Science Framework
Visual Attention and Saliency Detection
article

Real World Engagement Attention Dataset #1c (RWEAD #1c)

João M. F. Rodrigues, Mohamed El Afia
article en

Abstract

RWEAD #1c is a group-behaviour video dataset acquired in an approximately 57 m² room (~10.8 m × ~7.0 m) of a university building hall, configured as an Indoor-category exhibition stand within a building hall. The sensing infrastructure comprises a single RGB camera (1920 × 1080 px, 30 fps) mounted at an elevated viewpoint, 3.5 m high and tilted 20° downward, overlooking the entire area. The positions of the camera and of the three Areas of Interest (AoIs) -- Desk, Maquette and TV -- were logged in a coordinate table. Because the hall has a glazed façade, people outside the area remain visible after exiting; an indoor event-area mask therefore separates the indoor area from the outside, and entries and exits are defined as crossings of the two door lines. The participant cohort consists of seven amateur actors (P#1–P#7) aged 20–30, all university students without formal acting training. Each participant received a script and was allotted five minutes for memorisation prior to data acquisition. Fifteen scripted recordings were acquired, five for each construct: conversion, retention, and commitment. The scripts include "positive" and "negative" cases, as well as group dynamics such as groups merging and splitting, members leaving and rejoining their group, exits and re-entries, overlapping groups, and occlusion by a structural pillar.

Open Science Framework
University of Algarve (PT)
Openalex Percentile: Top 17%
Visual Attention and Saliency Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Real World Engagement Attention Dataset #1c (RWEAD #1c) — João M. F. Rodrigues, Mohamed El Afia · Open Science Framework (2026) | TGRS Research Map | TGRS