Intelligent CCTV Surveillance System For Real Time Suspicious Activity Detection Using Deep Learning
Bibliographic record
Abstract
This work proposes a CCTV system with the backup of artificial intelligence to detect unusual behavior in real time, improving comprehensive security surveillance. Unlike conventional CCTV supported by massive manual monitoring, the proposed system employs advanced AI techniques, including deep learning, to identify unusual human behaviors through video data assessment. The technology assists in alleviating the workload of security officers by issuing automated notifications when potential threats are identified. Tests conducted on the system indicate that the system is likely to detect activities such as unauthorized access, violence, and concealed items with accuracy. This review concludes that this AI solution can be utilized in public areas with a level of effectiveness in implementing security protocols and quick response to security incidents. Additionally, this integration ensures scalability across environments from offices to public areas. The system also addresses typical concerns like reducing false alarms and coping with difficult situations. Overall, this is a big leap towards more intelligent and efficient surveillance solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".