An Evaluation of a Haar Cascade Classifiers using Multi-Resolution Images and Multi-Threading Resources on a Raspberry Pi
Bibliographic record
Abstract
Image processing plays a crucial role in vision-based IoT sensors, serving various applications to enhance productivity. Researchers have highlighted computational challenges in object detection on low-cost devices like the Raspberry Pi. In today's fastpaced technological landscape, the need for automated systems delivering accurate results is paramount to task completion. This study introduces an effective multithreading approach for the Support Vector Machine (SVM) method. We have implemented a multithreading algorithm for the SVM recognition processes, harnessing the power of multicore CPU utilization. Our evaluation incorporates Memory usage, CPU Temperature, FPS, Confidence levels, and Elapsed time on the Raspberry Pi platform, with the primary goal of addressing real-time computation challenges using the Pi camera. The experimental results demonstrate a notable enhancement in detection confidence, affirming that multithreading significantly bolsters detection performance on Raspberry Pi processors across various image resolutions.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".