Real-Time Deep Learning-Driven Surveillance with Spatiotemporal Feature Extraction for Detection of Anomalous Human Behavior Across Dynamic Environments
Bibliographic record
Abstract
Nowadays continuous monitoring of public and private environments through Closed-Circuit Television (CCTV) is at peak attention.The identification and reporting of suspected human activity is crucial for the safety and security of the individuals and their belongings.Many researchers have provided numerous solutions for automated activity monitoring with CCTV and machine learning applications.But these models are struggling to provide high accuracy with real-time detection and triggering alert systems in a dynamic environment.The proposed model addresses these issues with a combined approach of convolutional and recurrent neural networks (Inception V3 and Bidirectional Long Short-Term Memory (BiLSTM)) with an attention mechanism to classify videos.This proposed model uses the strength of the Inception V3 network to extract spatial features from video frames, and the BiLSTM network processes these features in a timedependent manner to the identification of suspicious human activities.Also, the attention mechanism added to proposed system to focuses on the most significant spatiotemporal variables for violence detection.This deep learning model is designed to extract spatiotemporal features and thus can extract complex patterns in human motion robustly.This model is trained with publicly available dataset to analyze the performance with accuracy in a dynamic environment.The proposed deep-learning model effectively identifies and categorizes numerous suspicious behaviors in real-time by scrutinizing video sequences.The performance analysis proves that the proposed model efficiently detects activities like loitering, aggressive behavior, and unauthorized access.This automated surveillance system strengthens security in homes as well as other public and private environments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".