Subjective trajectory characterization: acquisition, matching, and retrieval
Bibliographic record
Abstract
We describe a system that automatically tracks moving objects in a scene and subjectively characterizes the object trajectories for storage and retrieval. A multi-target color-histogram particle filter combined with besthypothesis data association is the foundation of our trajectory acquisition algorithm. To improve computational performance, we use quasi-Monte-Carlo methods to reduce the number of particles required by each filter. The tracking system operates in real-time to produce a stream of XML documents that contain the object trajectories. To characterize trajectories subjectively, we form a set of shape templates that describes basic maneuvers (e.g., gentle turn right, hard turn left, straight line). Procrustes shape analysis provides a scaleand rotation-invariant mechanism to identify occurrences of these maneuvers within a trajectory. To add spatial information to our trajectory representation, we partition the two-dimensional space under surveillance into a set of mutually exclusive regions. A temporal sequence of region-to-region transitions gives a spatial representation of the trajectory. The shape and position descriptions combine to form a compact, high-level representation of a trajectory. We provide similarity measures for the shape, position, and combined shape and position representations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".