Abnormal Behavior Detection in Surveillance Systems Using a Hybrid EfficientNet-Transformer Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anomaly detection in video surveillance is vital for public safety, but challenges arise from the unpredictability of abnormal behaviors and large-scale systems. We propose a hybrid architecture combining EfficientNetV2S for efficient feature extraction with a transformer encoder to capture long-range dependencies through self-attention. This model robustly detects abnormal events by modeling local and global patterns in video frames. Evaluated on UCSD Ped1, UCSD Ped2, and Avenue datasets, our approach achieved accuracies of 99.51, 99.80, and 94.82, outperforming existing methods and proving their suitability for real-time smart surveillance applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it