Video Anomaly Detection With Probabilistic Modelling and Ensemble Learning on Deep Spatiotemporal Features
Bibliographic record
Abstract
ABSTRACT Video surveillance systems are commonly employed to monitor activities and ensure the safety and security of various environments. Anomaly detection in these systems is challenging due to the rarity and high variability of abnormal events. Integrating anomaly detection enables the identification of atypical or suspicious activities. This paper proposes a novel approach for video anomaly detection based on ensemble learning in a weakly supervised setting. The method consists of a two‐stage framework. In the first stage, spatiotemporal features are extracted from video data using 3D deep networks, followed by a multi‐scale attention module to enhance feature representation. Anomalous events are then identified by analysing discrepancies in probabilistic distributions, incorporating multi‐instance learning with a novel term in the loss function. In the second stage, the detection process is refined through ensemble learning strategies to optimise overall performance. The effectiveness of the proposed framework is demonstrated through extensive experiments on five benchmark datasets: UCF‐Crime, XD‐Violence, ShanghaiTech, CUHK Avenue, and UCSD Ped2. The method achieves frame‐level AUC scores of 97.89% on ShanghaiTech, 95.97% on CUHK Avenue, 97.38% on UCSD Ped2, 94.02 on XD‐Violence, and 80.86% on UCF‐Crime, showing competitive performance and highlighting the potential of ensemble‐based weakly supervised methods for video anomaly detection.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".