A COMPUTATIONAL-RAM (C-RAM) ARCHITECTURE FOR REAL-TIME MESH-BASED VIDEO MOTION TRACKING PART 1: MOTION ESTIMATION
Bibliographic record
Abstract
This paper presents a new Computational-RAM (C-RAM) architecture for real-time mesh-based video motion tracking. The motion tracking consists of two operations: mesh-based motion estimation and compensation. The proposed motion estimation architecture is presented in Part 1 and the proposed motion compensation architecture is presented in Part 2. The motion estimation architecture stores two frames and computes motion vectors for a regular triangular mesh structure as defined by MPEG-4 Part 2. 1 The motion estimation architecture uses the block-matching algorithm (BMA) to estimate the vertical and horizontal motion vectors for each mesh node. Parallel and pipelined implementations have been used to overcome the huge computational requirements of the motion estimation process. The two frames are stored in embedded S-RAMs generated with Virage™ Memory Compiler. The proposed motion estimation architecture has been prototyped, simulated and synthesized using the TSMC 0.18 μm CMOS technology. At 100 MHz clock frequency, the proposed architecture processes one CIF video frame (i.e., 352×288 pixels) in 1.48 ms, which means it can process up to 675 frames per second. The core area of the proposed motion estimation architecture is 24.58 mm 2 and it consumes 46.26 mW.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".