A system-level design for foreground and background identification in 3D scenes
Bibliographic record
Abstract
This paper proposes a system-on-chip (SoC) FPGA - based real-time video processing platform for background and foreground identification. Background and foreground identification is a co mmon feature in many tasks in video content analytics (VCA), including object detection, tracking, segmentation and recognition. VCA is a relatively new field in video processing; it has generally been implemented using two chips, with the image signal processing (ISP) part in a DSP or an FPGA and the VCA part executed by a processor. However, a new generation of SoC FPGAs that incorporates a processor and an FPGA into a single chip makes it possible for a single chip to perform both ISP and VCA. This study details the hardware implementation of a real-time background and foreground identification algorithm in an SoC, including the capture, processing and display stages. The proposed platform uses photometric invariant color, depth data and local binary patterns (LBPs) to distinguish backgrounds from foregrounds. The system uses minimal cell resources and tries to implement modules using a pipeline technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".