A robust motion detection and estimation filter for video signals
Bibliographic record
Abstract
The problem of detecting areas of motion in video sequences and estimating parameters such as speed, direction and dynamics is addressed in many applications of image processing such as video surveillance, object tracking, image stream compression or autonomous navigation systems. Real world computer vision highly depends on reliable, robust systems for recognition of motion cues to make accurate high-level decisions about its surroundings. In this paper, we present a simple, yet high performance low-level filter for motion detection and estimation in digitized video signals. The algorithm is based on constant characteristics of a common, 2-frame interlaced video signal, yet its applicability to generically acquired image sequences is shown as well. In general, our approach presents a computationally low-cost solution to motion estimation application and compares very well to existing approaches due to its robustness towards environmental changes. A simple application of motion parameter estimation based on a pedestrian surveillance application is illustrated.
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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 | 0.000 | 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".