Deep Learning for Retail Security: Obase’s End-to-End Shoplifting Detection Framework
Retail theft, or shoplifting, remains one of the most pressing challenges for the industry—costing retailers billions annually worldwide. Despite the spread of surveillance systems, most existing security methods still rely on manual monitoring, leaving room for human error and operational inefficiencies.
At Obase R&D Center, together with Özyeğin University, we have developed and published a groundbreaking deep learning-based framework that redefines how shoplifting can be detected in real time. Developed within the Government-funded TEYDEB 1501 program theft (fraud) detection project, this work has been published in the prestigious journal Signal, Image and Video Processing, published by Springer.
Our study introduces an integrated system with four components:
- Person Detection: Identifies when customers approach a shelf.
- Activity Recognition: Analyzes customer movements to detect suspicious behavior.
- Product Detection: Tracks which items are taken from shelves.
- Person Re-Identification: Matches suspicious individuals at checkout and verifies whether items are paid for.
Unlike previous single-model approaches, our end-to-end system ensures a multi-layered verification process, reducing false positives and enhancing real-world applicability. Tested on a dataset of over 1,200 videos collected from both demo and real retail environments, the framework achieved 95% overall accuracy, setting a new benchmark in retail security.
This research not only highlights the power of AI in addressing long-standing retail challenges but also reflects our commitment to developing practical, deployable, and ethical AI solutions. The system is designed to support store staff with actionable alerts rather than replace them, ensuring both operational efficiency and customer trust.
👉 You can read the full academic article here: Springer Link