Video-Based Drive-Around Detection in Retail
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Solution Overview
Problem
Current solutions for detecting customer drive-arounds in retail settings, which result in lost sales and increased traffic, are manual and lack automation, failing to effectively identify these events in real-time.
Innovation Solution
A method and system utilizing computer vision techniques to acquire and analyze images of retail premises, track customer entry and exit, and generate notifications for drive-arounds by monitoring parking spaces and drive-thru queues, with modules for customer entry detection, tracking, parking lot monitoring, drive-thru queue monitoring, and timing, to determine if a customer remains on the premises for a prescribed minimum time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual annotation is used to detect drive-arounds, then detection capability is provided, but automation is lacking and labor costs increase
Solution Approach 1:
The patent replaces manual annotation (mechanical human labor) with an automated computer vision system that uses image processing algorithms to detect drive-around events. The system automatically analyzes video footage from cameras positioned in the retail environment, identifying vehicles that enter and leave without completing transactions, thereby eliminating the need for manual review while maintaining detection capability.
Solution Approach 2:
The system enables self-service detection by implementing automated algorithms that independently identify and classify drive-around events without human intervention. The computer vision system processes video data, tracks vehicle movements, applies business rules to determine drive-around occurrences, and generates reports automatically, allowing the system to serve itself in the detection task.
2Productivity
If real-time detection is implemented, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The patent divides the drive-around detection system into distinct functional modules: video acquisition from cameras, pre-processing of video frames, object detection to identify vehicles, tracking to follow vehicle movements across frames, classification to determine drive-around events based on business rules, and report generation. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while enabling real-time processing.
Solution Approach 2:
The system performs preliminary actions by pre-defining business rules and criteria for what constitutes a drive-around event before actual detection begins. Configuration parameters such as time thresholds, vehicle identification criteria, and exclusion zones are established in advance, allowing the real-time detection system to simply apply these pre-set rules rather than making complex decisions during processing, thereby reducing computational complexity.
Data Source
AI summary
A system and method for detection of drive-arounds in a retail setting. An embodiment includes acquiring images of a retail establishment, analyzing the images to detect entry of a customer onto the premises of the retail establishment, tracking a detected customer's location as the customer traverses the premises of the retail establishment, analyzing the images to detect exit of the detected customer from the premises of the retail establishment, and generating a drive-around notification if the customer does not enter a prescribed area or remain on the premises of the retail location for at least a prescribed minimum period of time.