Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem.

Area of Science:

• Computer Vision
• Machine Learning
• Behavioral Analysis

Background:

• Retail shoplifting causes significant financial losses for businesses.
• Current deep learning models for shoplifting detection lack transparency, raising bias concerns and limiting legal admissibility.
• There is a need for accurate and explainable AI solutions in retail security.

Purpose of the Study:

• To develop a transparent model for automated shoplifting detection using social signal processing.
• To address the limitations of black box models in terms of understanding and legal acceptance.
• To achieve high accuracy in shoplifting prediction while maintaining model interpretability.

Main Methods:

• Development of a novel social signal processing model for shoplifting prediction.
• Training and validation using a custom dataset of manually annotated shoplifting videos.
• Comparison of the transparent model's performance against state-of-the-art black box methods.

Main Results:

• The social signal processing model demonstrates a high degree of understanding.
• The model achieves accuracy comparable to existing black box deep learning approaches.
• The developed model offers a transparent alternative for shoplifting detection.

Conclusions:

• Social signal processing offers a viable approach to creating transparent and accurate shoplifting detection systems.
• The developed model can help retailers mitigate losses while complying with legal standards.
• This research paves the way for more trustworthy AI applications in retail security.