Object-Centric Video Anomaly Detection with Covariance Features
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
In this paper, we propose four different methods for object-centric anomaly detection in surveillance videos based on autoregressive probability estimation. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. To decide whether an observation sequence (i.e. a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. By employing an object detection module to determine the important active regions in a scene with high detection rate, we propose new long-short term memory (LSTM), linear regression, and Gaussian mixture based methods to model the probability density of observation sequences. The most successful methods we propose achieves an average performance of 0.843 and 0.935 AUC scores respectively on two publicly available benchmark datasets.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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