Toward a Flexible System for Pedestrian Data Collection with a Microsoft Kinect Motion-Sensing Device
Bibliographic record
Abstract
Data on pedestrian activity, including volumes, walking speed, and trajectories, are used by transportation agencies and researchers for planning, design, and analysis. Several technologies are available for automatic collection of pedestrian data; however, all have inherent limitations in either functionality or monetary cost. Also, most technologies provide only counts. This paper proposes the use of an inexpensive motion-sensing device, Microsoft Kinect, which can track multiple people in low light conditions and can be combined with existing video-based daytime tracking. The tracking software and speed estimation methodologies are described, and indoor and outdoor studies show the system's effectiveness at determining pedestrian volumes and walking speeds. The accuracy of speed data is very satisfactory, with correlation of 98% or more for video data validation speeds. The accuracy of pedestrian volume data varies with traffic conditions; however, in low to moderate traffic conditions its performance is acceptable, with an undercounting error near 8%. The applications of the sensor and its complementarity with other sensors are discussed, as these are the first step toward a multisensor system.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".