Intelligent Recognition of Key Frame Target Behavior in Video Surveillance Based on Lightweight Convolution Neural Network
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 analysis and processing of massive surveillance videos, target behavior recognition is an important task. Most researchers pay more attention to the lightweight of convolution operators in intelligent recognition systems or increase the complexity of lightweight modules, but lack of lightweight research on point-by-point convolution modules which occupy a large number of parameters and computation. For this reason, this article carries out the research on intelligent recognition of key frame target behavior in video surveillance based on lightweight convolution neural network. The three-dimensional position information of bone joints is extracted as the target behavior feature. Based on local vector aggregation descriptor, it makes a more compact representation of key frames of the surveillance video, and gives the generation process of local vector aggregation descriptor. After the structured pruning of the filter, the memory occupation of the processed network model is significantly reduced, and the lightweight of the model is realized. Experimental results verify the effectiveness of the model.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it