Automatic Early Warning and Visual Analysis Framework for Sudden Health Incidents in Public Spaces Based on Multi-Source Video Streams and Behavior Recognition Algorithms
Bibliographic record
Abstract
With the acceleration of urbanization, the density of people in public spaces such as airports and shopping malls continues to rise.Sudden health incidents, such as fainting and acute cardiovascular events, pose a serious threat to public safety due to their sudden nature and short rescue window.Traditional manual monitoring is limited by labor costs and attention decay, making real-time and accurate early warning of such incidents difficult.The growing demand for intelligent public safety governance, coupled with advancements in artificial intelligence technologies, provides an opportunity for technological innovation in this field.Although existing video surveillance and behavior recognition technologies have been applied in public safety, they still face significant challenges in complex scenarios: crowd density and occlusion reduce the robustness of object detection and tracking, there is a lack of synchronization and fusion capabilities for multi-source video streams, the early warning system and visual analysis are disconnected, and the quality of specialized datasets limits algorithm optimization.To address these issues, this paper proposes an automatic early warning and visual analysis framework for sudden health incidents in public spaces, based on multi-source video streams and behavior recognition algorithms, and builds an end-toend intelligent analysis pipeline.The framework achieves multi-source video stream synchronization through a distributed access mechanism, and extracts multi-scale features using the DarkNet53 backbone network.An innovative cross-frame attention module is introduced to strengthen the target area expression by associating temporal features, improving detection stability in occlusion and deformation scenarios.A twin network is used to achieve precise target displacement prediction, and a data association strategy based on appearance similarity and motion consistency is employed to ensure the continuity and identity consistency of individual spatiotemporal trajectories.Finally, relying on multisource information fusion, the framework triggers multi-level automatic early warnings, and constructs a visual analysis platform with a Web GIS map, data dashboard, and interpretable video summaries to support emergency decision-making.This research provides a technical paradigm and practical solution for the intelligent management of sudden health incidents in public spaces, while enriching the theoretical and methodological applications of computer vision in the field of public safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".