A Hybrid Edge-Cloud Smart Door Surveillance System with Real-Time Risk Assessment and Secure Access Control
Bibliographic record
Abstract
This paper presents a hybrid edge-cloud smart door surveillance system designed to enhance residential security through real-time video analytics, anomaly detection, and dynamic access control. A local webcam captures live video streams processed at the edge by a Flask-Based server, integrating YOLOv8 object detection, HMM-based behavior anomaly detection, and facial recognition for identity verification. To guarantee uninterrupted operation without network connectivity, we also implement a fully offline face-recognition mode on the ESP32-CAM enabling the door to unlock locally under 100 ms. In parallel, a retrainable cloud-based Azure Custom Vision model detected object identities, assessed risk levels dynamically triggering local actions such as door control and real-time email alerts with contextual evidence. When risk levels exceed predefined thresholds, the system autonomously captures critical frames and sends alert or action-required notifications with attached visual evidence, using risk-specific visual theming. To address edge computing limitations in complex cases, recorded videos are analyzed by AWS Rekognition for comprehensive label detection. A responsive web interface provides live monitoring, event logging, OTP-based visitor access, and remote door operation, enhanced by dynamic UI feedback based on threat severity. This work demonstrates an integrated, scalable, and cost-efficient approach to edge-assisted smart security systems, laying a foundation for future improvements in multi-camera coordination ensuring the embedded firmware remains lightweight and easily updatable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".