Exploring the Need and Design for Situated Video Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual video analytics research, stemming from data captured by surveillance cameras, have mainly focused on traditional computing paradigms, despite emerging platforms including mobile devices. We investigate the potential for situated video analytics, which involves the inspection of video data in the actual environment where the video was captured [14]. Our ultimate goal is to explore the means to visually explore video data effectively, in situated contexts. We first investigate the performance of visual analytic tasks in situated vs. non-situated settings. We find that participants largely benefit from environmental cues for many analytic tasks. We then pose the question of how best to represent situated video data. To answer this, in a design session we explore end-users’ views on how to capture such data. Through the process of sketching, participants leveraged being situated, and explored how being in-situ influenced the participants’ integration of their designs. Based on these two elements, our paper proposes the need to develop novel spatial analytic user interfaces to support situated video analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it