Synchronized Multi-Camera Video Feed for Automated Shopping Session Tracking
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Solution Overview
Problem
Traditional shopping experiences in physical stores are inefficient due to long wait times for customers and idle time for cashiers, especially during non-peak hours, and lack of automation in monitoring and tracking customer purchases.
Innovation Solution
A virtual store tool that generates a virtual store layout and video feed from physical store cameras to emulate a shopping session, allowing for automated tracking and verification of customer selections, and enabling remote monitoring and payment processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional cashiers are used to process customer payments, then customers can receive personalized service, but cashiers spend significant time idle during non-peak hours
Solution Approach 1:
The system enables self-service through automated video analysis that tracks customer movements, identifies products taken from shelves, and automatically generates shopping carts and receipts without human cashier intervention. The video feed processing and purchase detection algorithms allow the system to serve customers independently.
Solution Approach 2:
The patent replaces the mechanical system of human cashiers with an automated video processing system using computer vision algorithms. The system substitutes human visual monitoring and manual cart generation with automated video analysis that detects customer actions and generates purchase records automatically.
2Measurement precision
If multiple cameras are deployed to monitor customer shopping sessions, then purchase tracking accuracy improves, but processing resources and system complexity increase
Solution Approach 1:
The system divides the video processing task into segmented operations: individual camera feeds are processed separately to detect specific events (product removal, customer movement), then results are integrated to form complete shopping session records. This segmentation allows parallel processing and reduces the computational burden on any single processing unit.
Solution Approach 2:
The system performs preliminary actions by pre-processing video feeds to extract key features and events before full analysis. Video frames are pre-analyzed to detect customer presence, product interactions, and movement patterns, which then feed into the main purchase detection algorithm, reducing the complexity of the overall processing task.
Data Source
AI summary
An apparatus includes an interface, display, memory, and processor. The interface receives a video feed including first and second camera feeds, each feed corresponding to a camera located in a store. The processor stores a video segment in memory, assigned to a person and capturing a portion of a shopping session. The video segment includes first and second camera feed segments, each segment corresponding to a recording of the corresponding camera feed from a starting to an ending timestamp. Playback of the first and second camera feed segments is synchronized, and a slider bar controls a playback progress of the camera feed segments. The processor displays the camera feed segments and copies of the slider bar on the display. The processor receives an instruction from at least one of the copies of the slider bar to adjust the playback progress of the camera feed segments and adjusts the playback progress.