Max-Sum-Based Data Associations for Tracking Point and Extended Targets
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Bibliographic record
Abstract
For multitarget tracking applications, data association is a fundamental problem of assigning measurements to their corresponding targets. In this article, we propose two algorithms for tracking point and extended targets, respectively, based on factor graph representations of the joint probability density functions. Both employ the max-sum (MS) algorithm to find the maximum a posteriori assignment such that the state of each target is updated with the most probable measurement(s). We model the single target densities as Gaussian distribution for point targets and gamma Gaussian inverse Wishart distribution for extended targets. Under linear Gaussian assumptions on the target models, the proposed algorithms provide analytical solutions to multitarget tracking problems. Specifically, the messages flowed in the factor graphs, existence probabilities and states of the targets are analytically calculated. These two algorithms have reduced computational load compared to the particle-based sum-product (SP) algorithms and avoid gating or clustering used by traditional multitarget tracking methods. We compare the proposed MS-based algorithms (MSAs) with the Poisson multi-Bernoulli mixture filters and the SP-based algorithms, and simulation results show that the MSAs have comparable or improved tracking performance.
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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 | 0.001 | 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