Dumbbell Calibration for a Multi-Camera Tracking System
Why this work is in the frame
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Bibliographic record
Abstract
Camera calibration is a preliminary and key work for building up a multi-camera visual tracking system. The purpose of calibration is to obtain transformations between several camera frames, so that each camera knows "where" the other cameras are with respect to itself. In this paper, we describe and implement a technique to determine the transformation of a two-camera tracking system by using a dumbbell as the calibration object. The dumbbell prop is novel and easy to be handled. By putting it within the common view field of two cameras, we firstly extract conies of dumbbell balls from raw images by color separation and ellipse fitting. The intrinsic and extrinsic parameters of each camera are recovered from the conies using linear approaches. Then, we present a method to calculate camera coordinates transformation based on the extrinsic parameters and the multiple view geometries. Experimental results are carried to further demonstrate the practicality of the proposed methods.
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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 | 0.000 |
| 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