Real Time Classification of Retail Theft Utilizing YOLO Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the rapid advancement of computer vision technologies, human behavior detection systems in surveillance environments have become a vital research area, especially in security applications such as shoplifting surveillance.This study aims to develop a classification model based on still images to detect suspicious behavior in a shopping environment.Current theft detection systems struggle with real-time processing; our YOLOv8-based framework addresses this by achieving 95% accuracy with low latency (12 ms per frame).This makes our approach suitable for being integrated in real-time monitoring systems where it guarantees an early and robust detection of abnormal behavior.Comprehensive comparison with other state-of-the-arts also confirms the superiority of the proposed method in terms of speed and detection accuracy.The model was trained using the UCF Crime dataset, as well as manually collected suspicious images from multiple sources.The categories comprised (normal behavior) and (suspicious behavior).The model was trained for 150 epochs and the model parameters were fine-tuned to obtain the best performance.Different performance metrics including precision, recall, F1-score, confusion matrix analysis, and visual results of the output of the model, were assessed.This paper is a first step in the development of an intelligent system for detecting suspicious instore behavior.This system will be extended to analyze temporal behavior with video sequences, with a view to providing a richer and more accurate understanding of theft pattern in its temporal context.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it