KianNet: A Violence Detection Model Using an Attention-Based CNN-LSTM Structure
Bibliographic record
Abstract
Violent behaviour is always an important issue that threatens any society. Therefore, many organizations have used surveillance cameras to monitor such events to preserve public safety and mitigate potential harm. It is difficult for human operators to monitor the copious camera feed manually, however, automated systems are employed to enhance the accuracy of violence detection and reduce errors. In this paper, we propose a novel model named KianNet that effectively detects violent incidents inside recorded events by combining ResNet50 and ConvLSTM architectures with a multi-head self-attention layer. The utilization of ResNet50 enables robust feature extraction, while ConvLSTM makes it easier to take advantage of the temporal dependencies in the video sequences. Furthermore, the multi-head self-attention layer enhances the model’s ability to focus on relevant spatiotemporal regions and their discriminatory capacity. Empirical investigations on large datasets UCF-Crime and RWF confirm that the proposed model outperforms its competitors.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".