A robust traffic state parameters extract approach based on video for traffic surveillance
Bibliographic record
Abstract
Vision-based sensors for traffic surveillance have attracted more attention because of their area sensing ability and flexibility. Conventional methods used to extracted vehicles mainly including background subtraction and images differences. Possible questions brought by these methods are time costuming and lack of robustness. Different from previous research, a new method based on wavelet transform is proposed. One dimension data set is generated from ROI of one frame in video. Vehicle edge is acquired from characterization of signals from wavelet transform. Furthermore, linear characterization used to represent edge of vehicles. Then combined geometry characterization and a dynamic criterion using histogram-based method are proposed to eliminate all unwanted shadow based on the linear characterization of edge. Speed of vehicles is obtained based on detective lines and minimum boundary rectangle (MBR), avoiding using Kalman filter to extract vehicle speed, reducing the huge computation. Experimental results show that the proposed method is more robust and accurate than traditional methods.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".