A New Automatic Vehicle Tracking and Detection Algorithm for Multi-Traffic Video Cameras
Bibliographic record
Abstract
Vehicle tracking systems are a vital tool in modern-day law enforcement and security operations.With the increasing threats of terrorism, organized crime, and illegal trafficking, monitoring and tracking suspicious vehicles has become a top priority for security agencies around the world.In this study, a target vehicle, which was described as suspicious, was tracked using the proposed vehicle tracking method that contains Gaussian Mixture Model (GMM) and Blob analysis.The same target vehicle was then detected using the Regions with Convolutional Neural Networks (RCNN), Faster RCNN, and You Only Look Once (YOLO) deep learning object recognition algorithms.In these applications, public traffic surveillance system images from the internet are used.Tracking is performed on images taken from more than one traffic surveillance system on the same road or route.The results from these methods were compared with each other, and the highest mean Average Precision (mAP) value was observed as 89.20% for the Faster RCNN algorithm using the Resnet101 deep learning architecture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".