Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes
Bibliographic record
Abstract
The study of crowd movement and behavioral patterns typically relies on spatio-temporal localization data of pedestrians.While monocular cameras serve the purpose, industrial binocular cameras based on multi-view geometry offer heightened spatial accuracy.These cameras synchronize time through circuits and are calibrated for external parameters after fixing their relative positions.Yet, the flexibility and real-time adaptability of using two different cameras or smartphones in close proximity, forming a short-baseline binocular camera, presents challenges in camera time synchronization, external parameter calibration, and pedestrian feature matching.A method is introduced herein for jointly addressing these challenges.Images are abstracted into spatial-temporal point sets based on human head coordinates and frame numbers.Through point set registration, time synchronization and pedestrian matching are achieved concurrently, followed by the calibration of the shortbaseline camera's external parameters.Numerical results from synthetic and real-world scenarios indicate the proposed model's capability in addressing the aforementioned fundamental challenges.With the sole reliance on crowd image data, devoid of external hardware, software, or manual calibrations, time synchronization precision reaches the submillisecond level, pedestrian matching averages a 92% accuracy rate, and the camera's external parameters align with the calibration board's precision.Ultimately, this research facilitates the self-calibration, automatic time synchronization, and pedestrian matching tasks for short-baseline camera assemblies observing crowds.
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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 | 0.000 |
| 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".