Intelligent Surveillance Across Multi-Domain Environments Using Deep Learning Architectures
Bibliographic record
Abstract
Today's surveillance systems need to do more than just record; they need to understand, react, and protect in real time in places like homes, schools, city streets, and corporate campuses. This paper talks about a unified deep learning framework that combines a number of smart functions, such as spotting objects and fire hazards, recognizing human actions, spotting drones and intruders, and flagging strange behaviour like falls or suspicious activity. We use models like YOLO, EfficientDet, MobileNetV2, ResNet-101, CNN-LSTM, ConvLSTM2D, LRCN, and I3D to learn about and study behaviour over time and space. The system can respond quickly and effectively, even in crowded or complicated scenes, thanks to attention mechanisms, skeletal tracking through Mediapipe, and edge computing. Testing in the real world shows that the system can recognize human activity with more than 98% accuracy and very few false alarms. In general, this method shows that there is a reliable and scalable way to meet the security needs of smart environments with intelligence and speed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".