Impact of stereoscopic vision and 3D representation of visual space on multiple object tracking performance
Bibliographic record
Abstract
Classical multiple object tracking (MOT) studies use 2D visual space representation. Also, they do not take into account stereoscopic vision capacity that allows better discrimination between the relative positions of multiple objects in space. However, our reality is a 3D world where multiple objects move at different depth position with different speeds. We have conducted several experiments to evaluate the impact of different non-stereoscopic and stereoscopic representation of space on MOT performance. Moreover, instead of measuring the number of targets that can be tracked, we have used a new kind of measure based on the evaluation of the greatest speed at which the observer is capable to track a set of moving targets (four targets). This kind of measure allows a more precise threshold measurement to discriminate the performance of two observers that can track the same number of targets. The results of our experiments have shown that, relative to the non-stereoscopic conditions, significantly better speed thresholds were obtained with the stereoscopic representations of space. These results suggest that to better conform to our reality, 3D representation of the visual space should be use to optimally measure MOT performance. Finally, contrary to the classical method using the number of objects tracked, the evaluation of the speed threshold for a set of moving target appears to be a better representative measure to differentiate MOT performance between individuals.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".