Geo‐spatial traffic behaviour analysis and anomaly detection for ITS applications
Bibliographic record
Abstract
Abstract Understanding the behaviour of traffic participants within the geo‐spatial context of road/intersection topology is a vital prerequisite for any smart ITS application. This article presents a video‐based traffic analysis and anomaly detection system covering the complete data processing pipeline, including sensor data acquisition, analysis, and digital twin reconstruction. The system solves the challenge of geo‐spatial mapping of captured visual data onto the road/intersection topology by semantic analysis of aerial data. Additionally, the automated camera calibration component enables instant camera pose estimation to map traffic agents onto the road/intersection surface accurately. A novel aspect is approaching the anomaly detection problem by AI analysis of both the spatio‐temporal visual clues and the geo‐spatial trajectories for all type of traffic participants, such as pedestrians, bicyclists, and vehicles. This enables recognition of anomalies related to either traffic‐rule violations, for example, jaywalking, improper turns, zig‐zag driving, unlawful stops, or behavioural anomalies: littering, accidents, falling, vandalism, violence, infrastructure collapse etc. The method achieves leading anomaly detection results on benchmark datasets World Cup 2014, UCF‐Crime, XD‐Violence, and ShanghaiTech. All the obtained results are streamed and rendered in real‐time by the developed TGX digital twin visualizer. The complete system has been deployed and validated on the roads of Helmond town in The Netherlands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".