Real-Time Target Tracking Library in Python
Why this work is in the frame
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Bibliographic record
Abstract
In a real-time tracking scenario, efficient tracking performance is particularly crucial given the computational power constraints inherent in low-cost, low-power sensors. The interval between successive radar measurements, known as the measurements per processing interval (MpPI), dictates the rate at which measurements are received. Since tracking algorithms operate recursively, each measurement must be processed to compute a full track before the next measurement arrives. Therefore, the efficiency of the tracker directly influences the achievable MpPI. Faster tracking algorithms enable higher MpPI operational modes, which are especially advantageous for scenarios like drone tracking, where objects move rapidly relative to the radar’s proximity. In this paper, we investigate the performance of a lightweight tracking library applying efficient software techniques, developed for use in onboard radar applications. In particular, we measured runtime profiles and track metrics including Generalized Optimal Sub-Pattern Assignment, Single-Integrated Air Picture and uncertainty metrics against Stone Soup, the leading open-source Python tracking library. A comparison of track performance and speed was completed by running both implementations of the Joint Probabilistic Data Association filter in various multi-target tracking scenarios. Results showed that our Standalone Tracker library software consistently outperformed Stone Soup in performance speed, while maintaining comparable track quality.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Software About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Software About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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