Compact Hardware of Running Gaussian Average Algorithm for Moving Object Detection Realized on FPGA and ASIC
Bibliographic record
Abstract
Real-time detection of moving object is essential to traffic monitoring, video surveillance and many other vital applications. The effectiveness of real-time detection hinges on background subtraction. This paper proposes a hardware-efficient architecture for the running Gaussian average (RGA) technique of background subtraction. The architecture was designed based on the field programmable gate array (FPGA) and application specific integrated circuit (ASIC). The FPGA was realized using Digilent ZedBoard, while the ASIC was implemented using Cadence Genus, Innovus, and Assura tools at 45 nm process technology. A prototype of our architecture was tested with a video containing multiple moving objects, and another containing a single moving object. The results show that the proposed architecture is efficient in terms of area, power and timing. Therefore, this paper provides a background subtraction approach that uses hardware resources effectively without sacrificing the detection accuracy.
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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) | 0.000 | 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 | 0.000 |
| 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".