Smart Crowd Monitoring and Suspicious Behavior Detection Using Deep Learning
Bibliographic record
Abstract
In the face of burgeoning population growth, ensuring security during public events, familial gatherings, and in high-traffic areas has become increasingly challenging.The manual monitoring of these areas, though facilitated by closed-circuit television (CCTV) cameras, often proves laborious and error-prone, leading to potential oversight of suspicious activities within crowds.To ameliorate this issue, an intelligent system for crowd monitoring and suspicious activity detection has been developed, utilizing deep learning algorithms.Specifically, the combined use of Fully Convolutional Networks (FCN) and Long Short-Term Memory (LSTM) was employed in the analysis of crowd behavior.Although previous attempts have been made to address this issue, the accuracy of such systems has remained a concern, often marred by false alarms and overlooked incidents.However, the present system exhibits a marked reduction in both false positives and negatives, boasting an accuracy of 97.84%, a significant improvement over existing model.This research proposes an effective solution to the problem of manual crowd monitoring, offering enhanced security outcomes through intelligent, automated surveillance.The high accuracy achieved underlines the potential of deep learning techniques in revolutionizing the field of surveillance, with further implications for crowd management and public safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".