Simple Real-Time Multi-face Tracking Based on Convolutional Neural Networks
Bibliographic record
Abstract
We present a simple real-time system that is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We employ a previously proposed cascaded Multi-Task Convolutional Neural Network (MTCNN) to detect a face, a simple CNN to extract the features of detected faces and show that a shallow network for face tracking based on the extracted feature maps of the face is sufficient. Our multi-face tracker runs in real-time without any on-line training. We do not adjust any parameters according to different input videos, and the tracker's run-time will not significantly increase with an increase in the number of faces being tracked, i.e., it is easy to deploy in new real-time applications. We evaluate our tracker based on two commonly used metrics in comparison to five recent face trackers. Our proposed simple tracker can perform competitively in comparison to these trackers despite occlusions in the videos and false positives or false negatives during face detection.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".