Hypotheses Generation and Verification Based Framework for Crowd Anomaly Detection in Single-Scene Surveillance Videos
Bibliographic record
Abstract
A two-stage framework for crowd anomaly detection in single-scene or scene-dependent surveillance videos is proposed in this article.The first stage generates several hypotheses corresponding to potential anomalous regions in a video frame and the second stage verifies them to reduce false alarms and identifies crowd anomalies.In the hypotheses generation stage, spatial and temporal derivatives are computed for each video frame and a saliency detector employing Hypercomplex Fourier Transform (HFT) is used to generate a saliency map.A threshold is applied to the saliency map to generate potential anomalous regions in the form of connected components.For each connected component, a set of 4 statistical features are computed and fed to the second stage which employs a Gaussian Mixture Model (GMM) as a verification method to yield the final crowd anomalies in the frame.The effectiveness of the proposed framework has been shown through results obtained on the UCSD anomaly detection benchmark dataset which contains two subsets namely Ped1 and Ped2 with a total of 48 test videos (9210 frames).Both frame-level and pixel-level anomaly detection results are provided using the widely recognized evaluation criterion in the domain and compared with the state-of-the-art methods.The experimental results show that the proposed framework obtains comparable results against the state-of-the-art methods.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".