Real-Time Vehicle Speed Detection Using Existing Traffic Camera Infrastructure with OpenGL ES
Bibliographic record
Abstract
Mobile phone processors have emerged as a key driving force in the evolution of embedded systems, thanks to their versatility, power efficiency, and cost-effectiveness. Low-cost platforms like the Raspberry Pi family are equipped with high-performance multi-core CPUs and GPUs, making them well-suited for real-time image processing tasks. This paper introduces a GPU shader method for detecting moving objects and measuring their speed using a constant-rate series of sequential images, such as live video feeds or recordings from existing traffic cameras. The system employs the industry-standard, non-vendor-specific OpenGL ES on affordable embedded systems. The CPU manages data flow to the GPU shaders, which identifies changes in pixels across frames to detect potential moving objects. The displacement of each object is then calculated, mapped into a practical distance, and returned to the CPU. This approach was implemented on a low-cost Raspberry Pi 4, successfully extracting speed data from 720p video at 10 FPS. Additionally, the code is easily portable to newer, faster embedded platforms, enabling even higher data rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".