Automated Collection of Pedestrian Data Using Computer Vision Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pedestrian data collection is critical for the planning and design of pedestrian facilities. Most pedestrian data collection efforts involve field observations or observer-based video analysis. These manual observations are time consuming, limited in coverage, resource intensive and error prone. Automated video analysis which involves the use of computer vision techniques can overcome many of these shortcomings. Despite advances in the field of computer vision applications for pedestrian detection and tracking, the technical literature shows little use of these techniques in pedestrian data collection practices. The likely reasons are the technical complexities that surround the processing of pedestrian videos. To extract pedestrian trajectories automatically from video, all road users must be detected, tracked at each frame and classified by type, at least as pedestrians and non-pedestrians. This is a challenging task in busy open outdoor urban environment. Common problems include global illumination variations, multiple object tracking and shadow handling. Specific problems arise when dealing with pedestrians because of their complex movement dynamics, varied appearance and non-rigid nature. The main objective of this study is to present a system for automated collection of pedestrian walking speed using computer vision techniques. The system is based on a previously developed feature-based tracking system for vehicles which was significantly modified to adapt to the particularities of pedestrian movement and to discriminate pedestrian and motorized traffic. The system was tested on real video data collected at Downtown area of Vancouver, British Columbia. This study is unique in so far as it tests the system under a variety of daylight conditions, crowd densities, movement context, and the video analysis approach. Promising results were obtained and several conclusions were drawn using statistical analysis of the automatically extracted pedestrian trajectories.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it