Automated person categorization for video surveillance using soft biometrics
Bibliographic record
Abstract
We present a prototype video tracking and person categorization system that uses face and person soft biometric features to tag people while tracking them in multiple camera views. Our approach takes advantage of temporal aspect of video by extracting and accumulating feasible soft biometric features for each person in every frame to build a dynamic soft biometric feature list for each tracked person in surveillance videos. We developed algorithms for extracting face soft biometric features to achieve gender and ethnicity classification and session soft biometric features to aid in camera hand-off in surveillance videos with low resolution and uncontrolled illumination. To train and test our face soft biometry algorithms, we collected over 1500 face images from both genders and three ethnicity groups with various sizes, poses and illumination. These soft biometric feature extractors and classifiers are implemented on our existing video content extraction platform to enhance video surveillance tasks. Our algorithms achieved promising results for gender and ethnicity classification, and tracked person re-identification for camera hand-off on low to good quality surveillance and broadcast videos. By utilizing the proposed system, a high level description of extracted person's soft biometric data can be stored to use later for different purposes, such as to provide categorical information of people, to create database partitions to accelerate searches in responding to user queries, and to track people between cameras.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".