Enhanced Vehicle Movement Counting at Intersections via a Self-Learning Fisheye Camera System
Bibliographic record
Abstract
Accurate vehicle counting at intersections is crucial for assessing traffic flow and gaining insights into vehicle trajectories captured by traffic cameras. This paper introduces an innovative framework that leverages a fisheye camera system to count vehicle movements at intersections with two significant contributions: First, the proposed algorithm employs a novel zone-based counting methodology to categorize and collect trajectory data and autonomously learn movement patterns with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm at intersections. Second, as the algorithm becomes proficient in recognizing the paths traversed by vehicles, it seamlessly transitions into a hybrid mode, integrating both zone-based and path-based counting techniques. It enables accurate vehicle counting even in challenging scenarios involving broken tracks or partial trajectories. The performance of the proposed method is accessed by conducting experiments on three real-world fisheye camera footage datasets. The results demonstrate the efficacy of our novel approach, achieving an impressive F1 score exceeding 98% across all tested intersections, underscoring its potential for real-world applications in traffic management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".