Group Association System Using Gesture and Behavior Analysis
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Solution Overview
Problem
Existing systems for identifying user groups in facilities, such as stores and hospitals, face challenges in accurately determining group membership, particularly when strict 'one scan, one user' protocols are not adhered to, leading to issues like tailgating and frontgating, which can result in incorrect billing and security breaches.
Innovation Solution
A system that uses cameras and scanners to analyze scan gestures, user location data, and behavior patterns to determine group associations, allowing for automated identification of groups without human intervention, and enabling guests to enter without separate enrollment, while ensuring accurate billing and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strict 'one scan, one user' protocol is enforced, then security and billing accuracy are improved, but user throughput is reduced and guest entry is hindered
Solution Approach 1:
The system automatically determines group membership and associates guests with authorized users without requiring manual intervention or separate enrollment processes. The automated image recognition and behavior analysis systems independently identify group associations, enabling self-service operation that maintains billing accuracy while improving throughput
Solution Approach 2:
The system changes the parameter from strict one-to-one scanning to flexible group-based scanning. By allowing multiple users to scan sequentially and using image data to determine group associations, the system maintains billing accuracy through automated group linking while significantly improving user throughput and entry speed
2Productivity
If automated group identification is implemented, then user throughput is improved, but system complexity increases
Solution Approach 1:
The scanning system performs multiple functions: it scans for authorized users, captures images of all users at the gate, determines group associations through behavior analysis, and processes billing. This multi-functionality consolidates what would otherwise require separate systems into a single integrated solution, improving throughput without proportionally increasing complexity
Solution Approach 2:
The system introduces image data and automated analysis as intermediaries between the scanning process and billing determination. Instead of direct one-to-one scanning, the image-based intermediary enables the system to identify group associations automatically, improving throughput while managing complexity through a mediating layer of automated recognition
3Device complexity
If manual group determination is used, then system complexity is reduced, but billing accuracy deteriorates due to tailgating and frontgating
Solution Approach 1:
The system replaces manual determination of group membership with automated image recognition and behavior analysis. Instead of relying on mechanical one-to-one scanning that is vulnerable to tailgating and frontgating, the system uses optical recognition and algorithmic analysis to automatically identify group associations, significantly improving billing accuracy while the automation manages complexity
4Productivity
If multiple entry points are provided, then user throughput is improved, but security monitoring becomes more difficult
Solution Approach 1:
The system merges the functions of multiple entry points into a unified automated recognition system. By capturing images and determining group associations at each entry point and then consolidating this data centrally, the system maintains the throughput benefits of multiple entry points while simplifying security monitoring through centralized automated analysis of all entry events
Data Source
AI summary
A facility may allow an authorized user to sponsor other users to enter. Actions taken by those users may be associated with the account of the authorized user. Sensor data obtained at a portal may be indicative of whether a user attempted to provide credentials to a sensor at the portal, time between entry of users, attributes about the user, and so forth. Information about when the users have exited may also be obtained. For example, if three people entered one after another and then left at the same time, they may be deemed to form a group. Based on this information, the actions taken by the users in the group may be associated with the account of the authorized user.