Real-time Pedestrian Classification System Using Deep Learning on a Raspberry Pi Cluster
Bibliographic record
Abstract
Convolutional neural networks (CNN) are commonly used for object classification. However, CNN is computationally expensive and can have performance issues in real-time applications. The objective of this research is to overcome these disadvantages through efficient design, implementation and deployment of CNN on a Raspberry Pi (Rpi) cluster for real-time pedestrian classification. Through the feasibility test, by running CNN classification on one Rpi 3 Model B, the processing speed of approximately 1 FPS was obtained. This is far from the human reaction time requirement which is set to be less than 0.5 sec. In this thesis, two solutions are proposed. First, architectural design, implementation and experimentation with a cluster composed of 3 RPis to meet the two main requirements. Second, tweaking and optimizing the design of the CNN itself. Through the combination of the two solutions, we could achieve the near real-time classification performances which are 0.16 seconds per image, 79.46\% accuracy and false negative rate of the classification results is only 4.08\%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".