Employee Recognition via Behavior Path Analysis
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Solution Overview
Problem
Existing methods fail to accurately distinguish employees from customers in physical spaces, such as retail stores, using video analysis, leading to biased customer behavior research due to the absence of effective employee recognition criteria and automated tracking systems.
Innovation Solution
A method and system utilizing computer vision technologies for automatic behavior analysis of employees in physical spaces, employing path analysis, spatial and temporal rules, and learning algorithms to differentiate employee behavior from customer behavior, allowing for employee recognition without requiring employees to carry devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If video analysis is used to monitor people in physical spaces, then behavior tracking capability is improved, but ability to distinguish employees from customers deteriorates
Solution Approach 1:
The patent segments the monitoring population into distinct categories (employees vs. customers) by analyzing specific behavioral patterns. It divides behavior analysis into multiple dimensions including spatial patterns (predefined areas, pathways), temporal patterns (dwell time, visit frequency), and interaction patterns (responsiveness to events). This segmentation enables the system to differentiate employee behaviors from customer behaviors through multiple independent criteria.
Solution Approach 2:
The patent changes the parameters used for behavior analysis from generic motion detection to employee-specific behavioral parameters. It introduces parameters such as dwell time in predefined areas, visit frequency to specific zones, responsiveness to predefined events, and adherence to spatial rules. These parameter changes transform the monitoring system from general-purpose to employee-specific recognition.
2Productivity
If automated behavior analysis is implemented, then productivity of monitoring is improved, but system complexity deteriorates
Solution Approach 1:
The patent creates a universal behavior analysis framework that can identify employees across different physical spaces and retail environments. The system uses a standardized set of behavioral criteria (spatial rules, temporal rules, event responsiveness) that can be applied universally regardless of the specific location or context. This multi-functionality allows the same system to operate effectively in various stores and environments without requiring custom development for each location.
Solution Approach 2:
The system implements self-service through automated rule-based analysis and machine learning algorithms that automatically learn and adapt to employee behavior patterns without manual intervention. The behavior analysis engine autonomously processes video data, applies predefined criteria, and identifies employees based on their behavioral characteristics, eliminating the need for manual monitoring or configuration for each new employee.
3Measurement precision
If employee recognition criteria are added to video analysis, then employee recognition accuracy is improved, but computational requirements deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining spatial areas, pathways, and behavioral rules before employee identification occurs. The system预先 establishes zones of interest, expected employee pathways, and temporal patterns that employees are likely to exhibit. This preliminary configuration allows the system to efficiently filter and analyze only relevant behaviors, reducing computational load during actual employee recognition while maintaining high accuracy.
Data Source
AI summary
The present invention is a method and system for recognizing employees among the people in a physical space based on automatic behavior analysis of the people in a preferred embodiment. The present invention captures a plurality of input images of the people in the physical space by a plurality of means for capturing images. The present invention processes the plurality of input images in order to understand the behavioral characteristics of the people for the employee recognition purpose. The behavior analysis can comprise a path analysis as one of the characterization methods. The path analysis collects a plurality of trip information for each tracked person during a predefined window of time. The trip information can comprise spatial and temporal attributes, such as coordinates of the person's position, trip time, trip length, and average velocity for each of the plurality of trips. Based on the employee recognition criteria applied to the trip information, the present invention distinguishes employees from non-employees during a predefined window of time. The processes are based on a novel usage of a plurality of computer vision technologies to analyze the behavior of the people from the plurality of input images.