Automated Analysis of Pedestrian Group Behavior in Urban Settings
Bibliographic record
Abstract
Movement trajectory of pedestrians, when tracked from video data, enables the automated analysis of individual's walking behavior. For example, speed preferences and walking strategies are typical behavior characteristics that benefit from this analysis. When pedestrians are walking in a group, they tend to adjust their speed and direction accordingly, while maintaining interpersonal distances. The adopted walking strategy leads to a coupling in their movement behavior. Such commonality, if considered, permits the discrimination between pedestrian groups and the distinction of pedestrians in different groups. Those are important factors when tracking a group of pedestrians or counting pedestrians in the crowd. The objective of this paper is localizing pedestrians in small groups using automated computer vision tracking. This paper describes the following tasks. First, to identify possible commonality in walking behavior between nearby pedestrians. This step is realized by proposing a new structural similarity measure of pedestrians' movement. Second, to provide a method for counting pedestrians in groups. A classification procedure accomplishes this task based on spatio-temporal criteria and the introduced movement similarity measure. Third, to show the feasibility of the method on a pedestrian group study from video data collected at a moderately dense pedestrian crosswalk in Vancouver, British Columbia. A validation of the group size classification demonstrated an accuracy of up to 77%. This paper enables a faster stream for comprehensive pedestrian data collection. Also, the new measure for group behavior can be useful when studying the mechanism of group formation and collision avoidance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".