Still Image Shopping Event Analysis System
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Solution Overview
Problem
Conventional video monitoring systems for in-store shopping events generate large amounts of data due to high frame rates, requiring significant time and resources for human analysis, making them costly and inefficient.
Innovation Solution
Implementing a still image shopping event analysis system that captures images at a low frequency and uses computer analysis to discriminate pixel changes between frames, allowing for the detection and analysis of shopping events, such as customer visits, purchases, and inventory management, without the need for human interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video monitoring systems are used to capture shopping events, then continuous monitoring capability is improved, but data storage requirements and processing costs increase significantly
Solution Approach 1:
The system captures still images at periodic intervals (e.g., every 5-30 seconds) rather than continuous video recording. This periodic sampling maintains monitoring capability while reducing data volume by approximately 90% compared to continuous video, directly resolving the contradiction between continuous monitoring and storage requirements
Solution Approach 2:
The system extracts only the essential information needed for shopping event analysis by capturing discrete still images rather than complete video streams. This extraction approach retains the ability to detect shopping events while eliminating redundant data, thereby reducing storage requirements while maintaining monitoring effectiveness
2Measurement precision
If conventional video monitoring systems are used to capture shopping events, then detailed behavior analysis capability is improved, but human analysis time and operational costs increase
Solution Approach 1:
The system replaces manual human analysis of video footage with automated computer-based image analysis algorithms. The processor automatically detects pixel changes between sequential images to identify shopping events, eliminating the need for human operators to manually review video and significantly reducing analysis time while maintaining detection accuracy
Solution Approach 2:
The system performs self-analysis through automated algorithms that process captured images and identify shopping events without human intervention. The computer automatically compares sequential images, detects changes, and generates analysis results, making the system self-sufficient and eliminating dependency on human analysts
3Quantity of substance
If low frequency still image sampling is used, then data storage requirements are reduced, but detection of fast shopping events may be compromised
Solution Approach 1:
The system dynamically adjusts the sampling frequency based on detected activity levels. During periods of high shopping activity, the capture rate increases to ensure fast events are detected, while during low activity periods, the rate decreases to minimize storage requirements. This adaptive parameter adjustment resolves the contradiction between storage efficiency and event detection capability
Data Source
AI summary
The still image shopping event analysis systems and methods provided herein may implement low frequency still image sampling and perform a computer analysis of the still images captured, including discriminating differences between frames of the still images based on changes of pixels between the frames and detecting and/or analyzing one or more shopping events based on the discriminated differences between frames of the still images. The systems and methods provided herein may further count and/or analyze the shopping events based on patterns of changes between frames, including for example, numbers of customers visiting and amounts of time customers spent visiting a shopping area, whether the visit was a transitory visit or involved more detailed shopping, whether a purchase occurred, and/or which and number of item(s) purchased.