AI Behavior Monitoring Using Proximity and Gaze Thresholds
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Solution Overview
Problem
Existing human behavior monitoring systems rely on human observation, which is expensive, prone to misinterpretation, and difficult to differentiate between normal and abnormal behavior, especially in large gatherings, and are susceptible to legal challenges.
Innovation Solution
An AI-based platform that uses a neural network to detect and measure features like proximity and coordinates of individuals and objects, determining behavior characteristics and generating notifications based on predefined criteria, eliminating the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observation is used to monitor behavior, then behavior detection can be performed, but it is expensive and subject to misinterpretation and errors
Solution Approach 1:
The patent replaces the mechanical system of human observation with an automated computer-based vision system. The system uses cameras to capture images, processes them through algorithms to detect behaviors, and eliminates the need for human observers. This substitution resolves the contradiction by providing reliable, error-free detection without human intervention complexity.
Solution Approach 2:
The system enables self-service behavior monitoring where the computer system automatically performs detection, analysis, and notification without human involvement. The automated system serves itself by capturing images, processing them through behavior detection algorithms, and generating notifications independently, thereby eliminating reliance on human observers.
2Reliability
If human observers intervene in public to identify potential shoplifters, then behavior detection can occur, but the target may claim civil rights violations and sue
Solution Approach 1:
The patent replaces human observers with an automated computer vision system that objectively detects behaviors without human bias or subjective judgment. This eliminates legal risks associated with human intervention while maintaining reliable behavior detection through algorithmic analysis of captured images and video feeds.
Solution Approach 2:
The system provides objective, data-driven feedback through automated detection and notification mechanisms. Rather than human judgment, the system uses predefined criteria and algorithms to determine suspicious behaviors, providing transparent and consistent feedback that reduces legal challenges.
3Productivity
If surveillance systems use human observation in large gatherings, then behavior monitoring is possible, but they have difficulty differentiating between normal and abnormal behavior on an individual basis
Solution Approach 1:
The patent segments the monitoring task by detecting and tracking individual persons separately within the crowd. The system identifies each person as a distinct object, analyzes their behavior independently, and differentiates between normal and abnormal behaviors on an individual basis, resolving the contradiction between crowd coverage and individual precision.
Solution Approach 2:
The system creates digital copies of individuals through captured images and video feeds, allowing each person to be analyzed as a separate entity. These visual copies enable precise individual behavior differentiation while maintaining comprehensive crowd monitoring coverage.
Data Source
AI summary
A system and method for automating a recorded observed behavior of at least one person is disclosed. The present invention records a video of the at least one person with respect to at least one object and detects at least one feature of the at least one person and the at least one object from at least one video image. Further, the present invention measures one or more parameters, wherein the one or more parameters correspond to a proximity between the at least one feature of the at least one person and the at least one object, eye coordinates, head coordinates. Based on which, the present invention automatically determines and notifies behavior characteristics of the at least one person by detecting at least one of the proximity meets a predefined threshold criteria, the eye coordinates and the head coordinates meet a pre-defined threshold coordinates.