Shopping Basket Vision Monitoring for Pushout Theft Detection
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Solution Overview
Problem
Existing cart containment systems struggle to differentiate between empty and loaded shopping carts, leading to false alarms and inefficiencies, especially in retail environments with mobile payment systems, and lack effective methods to prevent 'pushout' theft without costly hardware installations.
Innovation Solution
A system utilizing computer vision and machine learning to analyze images of shopping carts to determine load status, combined with wireless communication and anti-theft mechanisms, such as wheel locking, to prevent theft and reduce false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing cart containment systems use simple wire boundaries and wheel sensors to detect cart exits, then the system complexity is low, but the system cannot differentiate between empty and loaded carts causing false alarms
Solution Approach 1:
The patent replaces mechanical sensing systems (wire boundaries, wheel sensors) with computer vision technology. Cameras capture images of carts at exit points, and image processing algorithms analyze the visual data to determine cart load status. This substitution enables accurate differentiation between empty and loaded carts while avoiding the complexity of sophisticated mechanical detection systems.
Solution Approach 2:
The patent introduces an intermediary image processing system between the camera and the cart detection decision. The image processing unit analyzes visual data to determine load status, which then informs the cart containment decision. This intermediary layer enables precise load detection without requiring direct complex interaction with the cart structure itself.
2Reliability
If the system activates anti-theft actions for all exiting carts, then theft prevention coverage is maximized, but false alarms increase reducing operational efficiency
Solution Approach 1:
The patent applies different response strategies based on the local quality of the cart load status. Loaded carts trigger anti-theft actions (wheel locking, alarm activation), while empty carts are allowed to exit without intervention. This differentiated response eliminates false alarms for empty carts while maintaining theft prevention for loaded carts, improving operational efficiency.
Solution Approach 2:
The system uses feedback from image processing to adjust the anti-theft response. The image analysis provides real-time feedback on cart load status, which feeds back into the decision-making process to determine whether to activate anti-theft measures. This feedback mechanism ensures that anti-theft actions are only taken when necessary, reducing false alarms and improving operational efficiency.
3Measurement precision
If the system implements sophisticated load detection capabilities, then theft detection accuracy improves, but hardware costs increase
Solution Approach 1:
The patent replaces expensive mechanical and electronic sensing systems with computer vision technology. Standard cameras and image processing algorithms provide accurate load status detection at a lower hardware cost than sophisticated mechanical sensors, wireless communication systems, or complex electronic detection devices would require.
Solution Approach 2:
The patent uses visual copying (images) of the cart to determine load status. Instead of directly measuring or sensing the cart's load through complex hardware, the system creates visual copies (photographs) of the cart and analyzes these copies to infer load status. This copying approach provides accurate detection at lower hardware cost.
Data Source
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AI summary
A system for monitoring shopping baskets (e.g., baskets on human-propelled carts, motorized carts, or hand-carried baskets) can include a computer vision unit that can image a surveillance region (e.g., an exit to a store), determine whether a basket is empty or loaded with merchandise, and assess a potential for theft of the merchandise. The computer vision unit can include a camera and an image processor programmed to execute a computer vision algorithm to identify shopping baskets and determine a load status of the basket. The computer vision algorithm can comprise a neural network. The system can identify an at least partially loaded shopping basket that is exiting the store, without indicia of having paid for the merchandise, and execute an anti-theft action, e.g., actuating an alarm, notifying store personnel, activating a store surveillance system, activating an anti-theft device associated with the basket (e.g., a locking shopping cart wheel), etc.