Extended Target Tracking Using ET‐PMHT for 3D Convex Polytope Shapes With Partial Visibility
Bibliographic record
Abstract
ABSTRACT This article discusses the problem of tracking a single 3D extended target (or widely separated targets) with convex polytope shape when the target may only be partially visible. An extended target (as opposed to a point target) may generate multiple measurements in a single frame. With the advent of high‐resolution sensors (such as LiDAR), the targets need to be considered as extended targets and their shape as well as kinematics need to be estimated. The extended target may only be partially visible (self‐occlusion) and the measurements occur only from the visible parts of the target. In this work, different parts of a single extended target are assumed to be different targets constrained by the rigid body motion of the whole target, and the multitarget tracking framework is used to handle the tracking. The target shape is described using a convex hull represented by its vertices and a Delaunay triangulation. The point target PMHT is modified to develop an extended target PMHT (ET‐PMHT) joint association and filtering by assuming that the face triangulations are separate targets. Face management is incorporated into the algorithm to delete erroneous faces and the algorithm is able to add new faces to refine the shape estimate. The framework can handle self‐occlusion (partial visibility) by associating measurements only to the visible parts of the target. The algorithm's performance is compared with the 3D Gaussian Process under various scenarios, and RMSE of the centre, velocity and IoU metrics are used to quantify the performance. The proposed algorithm is able to outperform the 3D Gaussian Process in the centre RMSE metric by about 40% while achieving an IoU of 0.6 (on average) even when the target is only partially visible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".