A Cross-Domain Abnormal Behavior Recognition Model and Application Based on Transfer Learning
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 the realm of public safety early warning monitoring, swiftly establishing an efficient abnormal behavior recognition model is of significant importance.We propose and implement a video-based abnormal behavior recognition model for public safety early warning, leveraging transfer learning.The model's image features are transferred from ResNet18 to enhance adaptability and reduce training costs, while abnormal behavior features are obtained through training on the UCSD dataset.We provide a detailed introduction to the basic concepts and theoretical foundations of transfer learning, describes the model design and training process, and successfully constructs an abnormal behavior recognition model through experiments with transfer learning and the UCSD dataset.The experimental results demonstrate the model's superior adaptability and accuracy, offering substantial theoretical and practical value in the field of public safety early warning.This study not only fills the gap in cross-domain abnormal behavior recognition technology but also provides a new path for the rapid establishment of highly adaptable abnormal behavior recognition models, showcasing significant application value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it