Two-level automatic multiple target joint tracking and classification
Bibliographic record
Abstract
Target classification is of great importance for modern tracking systems. The classification results could be fed back to the tracker to improve tracking performance. Also, classification results can be applied for target identification, which is useful in both civil and military applications. While some work has been done on Joint Tracking and Classification (JTC), which can enhance tracking results and make target identification feasible, a common assumption is that the statistical description of classes is predefined or known a prior. This is not true in general. In this paper, two automatic multiple target classification algorithms, which can automatically classify targets without prior information, are proposed. The algorithms learn the class description from the target behavior history. The input to the algorithm is the noisy target state estimate, which in turn depends on target class. Thus, class description is learnt from the target behavior history rather than being predefined. This motivates the proposed two-level tracking and classification formulation for automatic multiple target classification. The first level consists of common tracking algorithm such as the Joint Probability Data Association (JPDA), the Multiple Hypothesis Tracking (MHT) or the Probability Hypothesis Density (PHD) filter. In the second level, a Mean-Shift (MS) classifier and a PHD classifier are applied to learn the class descriptions respectively based on the state estimations from the first level tracker. The proposed algorithms only require the kinematic measurements from common radar. However, feature information can be easily integrated. Besides theoretical derivations, extensive experiments based on both simulated and real data are performed to verify the efficiency of the proposed technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".