Multi-camera tracking by joint calibration, association and fusion
Bibliographic record
Abstract
To perform surveillance using multiple cameras, camera calibration, measurement-to-object association, fusion of measurements from multiple cameras are three essential components. While these three issues are usually addressed separately, they actually have mutual effects on each other. For example, calibration requires correctly associated objects and measurements with calibration errors will result in wrong associations. In this paper, we present a novel joint calibration, association and fusion approach for multi-camera tracking. More specifically, the expectation-maximization (EM) algorithm is incorporated with the extended Kalman filter (EKF) to give a simultaneous estimate of object states, calibration and association parameters. The real video data collected from two cameras are used to evaluate the tracking performance of the proposed method. Compared to the conventional methods, which perform calibration, association and fusion separately, it is shown that the proposed method can significantly improve the robustness and the accuracy of multi-object tracking.
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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 | 0.000 | 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 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".