DTV: Detection, Tracking and Validation Framework for Unique People Count
Bibliographic record
Abstract
Counting the unique number of people in a video (i.e., counting a person only once while the person is within the field of view), is required in many significant video analytical applications, such as transit passenger and pedestrian volume count in railway stations, shopping malls and road intersections and many others. In this paper, a novel framework is proposed for counting passengers, mainly in a railway station. The framework has three components: people detection, tracking and validation. In the detection step, a person is detected when he or she enters the field of view. Then, the person is tracked by optical flow based tracking until the person leaves the field of view. Finally, the trajectory generated by the tracker is validated through a spatiotemporal validation technique. The number of valid trajectories denotes the number of people. The novelty of the framework is the inclusion of the validation step, which is overlooked by the existing methods. Extensive experiments have been conducted on the datasets having both top views and whole body views of the passengers. Experimental results demonstrate that the proposed framework generates more than 90% accuracy on both types of views. It also detects and tracks persons having different hair colors and wearing hoodies, caps, long winter jackets, carrying bags and more. The proposed algorithm shows promising results also for people moving in different directions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".