Research on Employee Abnormal Behavior Detection Algorithm Based on Improved SSD
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
Detection of employee abnormal behavior is a hot topic in the field of video surveillance. In order to improve the accuracy and real-time performance of detection, this paper proposes an employee abnormal behavior detection algorithm based on SSD and improves the SSD algorithm. The improved SSD algorithm can effectively detect abnormal behaviors such as long-term immobility, sudden increase in activity, abnormal aggregation, abnormal body language and abnormal work performance. This paper introduces the technical route of enhanced SSD algorithm, including data preprocessing, network structure improvement, feature extraction, multi-scale prediction, target detection head design, loss function definition, post-processing technology, model training strategy and so on. The introduction of advanced feature fusion technology and the optimization of network structure make the improved SSD algorithm improve in three key performance indexes: detection time, detection accuracy and energy efficiency. Experimental results show that the maximum detection time of the improved algorithm is only 900ms, and the detection accuracy is 77.5%-95.4%. With the improvement of the energy efficiency ratio from 2.5 to 4.5, the changes of these indicators are very important for real-time monitoring system, which can greatly shorten the response time and reduce energy consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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