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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".