Analyzing surveillance videos using automatically generated processing sequences with knowledge-augmented genetic algorithms
Bibliographic record
Abstract
Extracting meaningful information from large number of video streams require designing specific algorithms to detect each type of object such as faces, people, vehicles, bags etc. The development of such specific algorithms requires a large amount of time from an expert in image analysis. Optimization based techniques have been increasingly used to automatically develop such algorithms, but they do not utilize any domain knowledge. Consequently, these automated approaches explore a large solution space and were only able to use a small number of primitive tools as building blocks in the generated algorithms. We proposes a novel method which integrates abstract knowledge about image processing tools into a genetic algorithm by exploiting the fact that there are classes of image processing algorithms that implement specific categories of algorithms such as noise reduction, sharpening, edge detection, binarization, classification etc. Using such knowledge, we were able to constrain the search performed by the genetic algorithm within a rich space of possibly successful processing sequences. Moreover, the use of abstract knowledge decouples the proposed method from implementation details of specific processing tools so that the system can be easily extended by incorporating additional tools. Experimental evaluations compare the our approach with a traditional genetic algorithm based implementation which does not utilize highlevel knowledge. A case study shows that the proposed method could converge to the optimum solution six times faster than the traditional method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".