Connected Home Visitor Classification for Tailored Autonomous Responses
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Solution Overview
Problem
Existing home surveillance systems generate a single type of alert for all detected persons at entryways, which is inadequate for distinguishing between welcome guests, unwelcome visitors, and passersby, leading to inefficient user interaction and increased processor and power usage.
Innovation Solution
A home monitoring system that classifies visitors into predefined groups, automatically initiating tailored responses such as two-way communication, adjusting security settings, and alerting authorities without user intervention, thereby reducing incorrect interactions and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single type of alert is used for all detected persons, then the system is simple to operate, but it cannot distinguish between welcome guests, unwelcome visitors, and passersby
Solution Approach 1:
The system segments visitors into distinct categories (welcome guests, unwelcome visitors, passersby) based on their behavior patterns, approach speed, and interaction characteristics. This segmentation enables the system to apply different alert types and response strategies for each visitor category, resolving the contradiction between operational simplicity and adaptability.
Solution Approach 2:
The system dynamically adjusts its response based on real-time analysis of visitor behavior. By continuously monitoring and adapting to visitor characteristics such as approach speed, duration of presence, and interaction patterns, the system maintains simplicity in operation while achieving high adaptability in visitor differentiation.
2Productivity
If the system automatically interacts with all visitors, then visitor assistance is improved, but processor usage and power consumption increase
Solution Approach 1:
The system applies different levels of automated interaction based on the local quality of each visitor situation. For welcome guests, the system provides comprehensive automated assistance; for passersby, it uses minimal processing; and for unwelcome visitors, it activates security protocols. This localized quality approach optimizes both productivity and energy consumption.
Solution Approach 2:
The system changes processing parameters dynamically based on visitor classification. By adjusting processor allocation, response time, and interaction depth according to the visitor type and situation urgency, the system achieves high interaction efficiency while minimizing unnecessary processor and power usage.
3Measurement precision
If the system provides detailed classification of visitors, then interaction accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary classification actions by pre-defining visitor categories and their corresponding characteristics. This preliminary structuring enables accurate visitor differentiation without requiring complex real-time analysis, as the system compares observed behavior against pre-established criteria for each visitor type.
Solution Approach 2:
The system introduces an intermediary processing layer that simplifies the complexity of visitor analysis. This intermediate layer aggregates and processes raw sensor data into standardized visitor profiles, making the overall system more manageable while maintaining high measurement precision in visitor classification.
Data Source
AI summary
The disclosure describes techniques for interacting with visitors at a visitor interaction system. A method includes obtaining video data captured by the visitor interaction system, identifying an approach of a person, and analyzing the video data to determine an identity of the person. The method includes automatically and without user intervention determining that the person belongs to a visitor groups of a plurality of visitor groups. Each of the visitor groups corresponds to a response model that includes a plurality of response actions. At least one of the response actions includes an autonomous response action. The method includes identifying a response model corresponding to the visitor groups and initiating an autonomous response action associated with the response model. Initiating the autonomous response action including presenting a message to the person, monitoring a response to the message, and sending a report message to the user via a client device.