Online Video Anomaly Detection Methodology With Highly Descriptive Feature Sets
Bibliographic record
Abstract
This paper presents a novel methodology for online video anomaly detection. The proposed algorithm divides each video sequence into non-overlapping cuboids, and assigns a state-of-the-art feature vector to each of them. The incoming patterns in testing phase will be then evaluated based on their similarity to the learned patterns. The first achievement of the proposed method is to introduce and apply highly descriptive features and build a histogram of vertical component of optical flow for different regions of the scene. Since the vertical component of optical flow contains both information of magnitude and orientation, it can be considered as an abstract feature rather than using magnitude and orientation, separately. As a result, the dimension of feature vector decreases which leads to reduce the complexity of entire system. The entropy of vertical component is also considered, and hence the differences in velocity and direction of the movements will be monitored. Finally, an efficient technique for anomaly detection search is presented that makes the proposed algorithm an applicable candidate for online performance. The simulation results on UCSD and UMN data sets confirm that the proposed methodology achieves high performance results in case of accuracy and total processing time compared with counterpart approaches.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".