FPGA implementation of the histogram of oriented 4D surface for real-time human activity recognition
Bibliographic record
Abstract
This paper presents a system-on-chip field gate programmable array (FPGA)-based, real-time video processing platform for human activity recognition in 3D scenes. The study details the hardware implementation of real-time human action recognition in 3D scenes, with the idea of iterative computing of a histogram of oriented 4D surface normal with an FPGA circuit. Recently, the rapid growth of modern application-based computer vision algorithms has further improved research and implementation. Especially, various accelerators such as histogram-oriented gradient (HOG), histogram-oriented flow (HOF), and histogram-oriented gradient depth (HOD) have been proposed based on the FPGA platform because it has the advantages of high performance and parallel operation. Although proposed FPGA accelerators have good performance in 2D scenes, the accelerator design space has not been well-exploited in 3D scenes. This study details the hardware implementation of a real-time human action recognition algorithm in 3D scenes, including the capture, processing, and display stages.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".