Bioptic Barcode Reader with AI Object Recognition
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Solution Overview
Problem
Conventional security measures at retail venues are limited in detecting theft events such as ticket switching, scan avoidance, and sweethearting, especially in high-traffic locations with large quantities of products, due to reliance on single field of view (FOV) systems.
Innovation Solution
Implementing multiple FOV systems with artificial intelligence-based object prediction and recognition to detect and prevent theft by analyzing images from multiple angles and using trained object recognition models to identify target objects and verify barcode information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single FOV system is used for monitoring target objects, then the device complexity is reduced, but the ability to detect theft events such as ticket switching and scan avoidance deteriorates
Solution Approach 1:
The patent transitions from a single FOV (two-dimensional monitoring) to multiple FOVs (three-dimensional spatial coverage), enabling the system to detect theft events from multiple angles and perspectives simultaneously, thereby resolving the contradiction between system simplicity and detection reliability
Solution Approach 2:
The monitoring system is segmented into multiple independent FOV units, each capable of detecting specific theft behaviors. This segmentation allows the system to maintain相对较低的 individual unit complexity while achieving high overall detection reliability through coordinated multi-FOV operation
2Reliability
If multiple FOV systems with AI-based object recognition are implemented, then the theft detection capability is improved, but the device complexity increases
Solution Approach 1:
The AI-based object recognition model serves multiple functions simultaneously: it identifies target objects, verifies barcode information, detects theft behaviors, and provides predictive analytics. This multi-functionality reduces the need for separate specialized systems, thereby limiting the increase in device complexity while maintaining high theft detection capability
Solution Approach 2:
The trained object recognition model acts as an intermediary between the multiple FOV imaging systems and the theft detection logic, consolidating complex image processing and analysis functions into a single AI module that simplifies the overall system architecture
3Ease of operation
If conventional single FOV POS terminal imaging systems are used, then the ease of operation is maintained, but the measurement precision of target object identification deteriorates
Solution Approach 1:
The system adds spatial dimensions by implementing multiple FOVs that capture target objects from different angles and positions, significantly improving measurement precision for identifying target objects and detecting theft behaviors without complicating user interaction
Data Source
AI summary
Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.