Associative Tracking and Recognition in Video
Bibliographic record
Abstract
Due to limited resolution and quality of surveillance video, tracking and recognizing of objects in surveillance video requires techniques for accumulation of information about the object over time. The simplest of these techniques is histograms, which computes the distribution of pixel values over time and which is frequently used for tracking uniformly coloured objects such as faces. Another well-known technique for the purpose is correlograms, which learns pixel values and their spatial relationship to yield better discriminative power. However, this technique also lacks a comprehensive learning ability, because it updates the information about the object, expressed in terms of cooccurance matrices, using the currently observed pixels only and ignoring the preceding learning history. Associative neural network based memorization can thus be seen as a more sophisticated mechanism for accumulation of information about an object in a video sequence. This presentation describes how to use the Open Source Associative Neural Network code for tracking and recognition of objects in video. Two demos showing multiple-face tracking and classification in low-resolution video are shown.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".