Enhancing Safety and Security: Face Tracking and Detection in Dehazed Video Frames Using KLT and Viola-Jones Algorithms
Bibliographic record
Abstract
In the context of safety and security, the ability to track and identify faces in hazy conditions presents a significant challenge.The deleterious effects of haze on video quality, such as the diminution of detail, reduction in contrast, distortion of color, and complications in depth estimation, impede effective facial recognition.Additionally, the complexity of live video tracking is exacerbated by factors such as occlusion, positional variations, and lighting changes.Despite these challenges, video sequences offer an abundance of information, surpassing static images in terms of potential data extraction.In this study, a dual approach strategy is employed to detect and track faces in hazy conditions.The Kanade-Lucas-Tomasi (KLT) algorithm, celebrated for its adept feature tracking capabilities, is deployed to execute face tracking.The effectiveness of this algorithm lies in its ability to accurately trace points across successive image frames, a crucial aspect of reliable face tracking.Concurrently, the Viola-Jones algorithm is utilized for face detection.The algorithm harnesses Haar-like features to efficiently discern faces in real-time, effectively overcoming the challenge of identifying faces within video frames.To further enhance the quality of the video, the dark channel prior (DCP) image dehazing technique is employed.This technique improves visibility by increasing contrast and color saturation, whilst concurrently identifying and eliminating air haze from the video frames.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".