Integrating Color and Gradient into Real-Time Curve Tracking and Feature Extraction for Video Surveillance
Why this work is in the frame
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Bibliographic record
Abstract
Efficient curve detection and feature extraction is a very important step in many videorelated applications, such as video content analysis and representation, surveillance systems, medical diagnoses, etc. For example, in video surveillance systems, curve tracking and feature extraction can be used in detecting moving targets from a video, allowing potential interesting events to be identified and analyzed for surveillance purposes. Curve detection usually includes edge detection and post processing procedures such as thinning, curve fitting or edge following, etc. Curve detection can significantly reduce less important data in a video frame while preserving structural information. Perceptual features can be extracted from curves for analysis or recognition purpose. However, Conventional edge detectors provide only an output of edge pixels. It is difficult to extract perceptual features directly from the edge detection results. Post-processing is then needed to remove noise, fill gaps, and fit edge pixels into curves. Unfortunately, most post-processing is too timeconsuming for use in real-time applications Most edge detection techniques fall into two categories, gradient based methods and second order methods. Gradient-based methods detect edges based on the first derivative of the intensity. Examples include the Sobel, Prewitt, Roberts, and Canny operators, in which the Canny operator (Canny 1986) is the one of most commonly used edge detector. The second order methods find edges by searching for zero crossings in the second derivative of the intensity. Examples of the second order methods include the Laplacian, Marr-Hildreth operators, etc. In color images, the color information also can be used to determine discontinuities in the color space Perez and Kock claimed in The edges with small hue change are removed from the Canny detector output in A compass operator is proposed in A 2D edge detection functional is used in
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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 | 0.001 |
| 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 it