Real-Time Intelligent Surveillance with YOLOv8 for Threat Detection and DenseNet121 for Activity Recognition
Bibliographic record
Abstract
The increasing complexity of global security threats necessitates advanced surveillance systems capable of real-time threat detection and forensic analysis. This paper presents an AI-driven surveillance framework integrating YOLOv8 for weapon detection and personnel classification, DenseNet121 for activity recognition module. The system features a scalable backend powered by Django and MySQL, while a Qt Designer and HTML and CSS-based frontend ensures seamless monitoring and automated alerting. Experimental results show robustness in challenging conditions such as low-light environments, and adaptability in various weather conditions. The weapon detection model achieved mean average precision at 50 percent threshold of 0.858, with a precision of 0.872. Personnel classification achieved mean average precision at 50 percent threshold of 0.947, with a precision of 0.946. Activity recognition exhibited reliable performance, classifying various threat activities in CCTV footages. This research addresses scalability, adaptability, and ethical compliance, contributing to the advancement of real-time surveillance technologies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".