Home Automation Training for Staged Similar-Object Identification
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Solution Overview
Problem
Property monitoring systems face challenges in distinguishing between similar target objects, such as pets or vehicles, leading to inconsistent automated actions and high computational resource usage.
Innovation Solution
The system uses sensor data to determine a similarity threshold for multiple predetermined objects, accesses higher resolution data to confirm the target object, and selects an automated action based on machine learning and historical data to perform precise actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the property monitoring system continuously monitors and transmits video data to distinguish between similar target objects, then the measurement precision is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system performs partial monitoring by only activating full video transmission and detailed analysis when a target object is detected within the field of view, rather than continuously transmitting all video data. This selective action reduces energy consumption while maintaining the ability to accurately identify similar objects when needed
Solution Approach 2:
The system first detects the presence of a target object in the field of view before initiating detailed identification processes. This preliminary detection step allows the system to prepare for accurate identification of similar objects only when necessary, reducing unnecessary energy consumption from continuous monitoring
2Measurement precision
If the property monitoring system continuously monitors and transmits video data to distinguish between similar target objects, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The system transmits only partial video data (when targets are detected in field of view) rather than continuously transmitting all video streams, reducing data transmission time while maintaining the ability to accurately identify similar objects when needed
3Device complexity
If the property monitoring system uses basic object detection to identify target objects, then the device complexity is reduced, but the measurement precision deteriorates
Solution Approach 1:
The system segments the monitoring process into distinct stages: field of view detection, target object identification, and differentiated action selection. This segmentation allows the use of simpler detection methods for basic monitoring while enabling more precise differentiation when targets are identified, resolving the contradiction between system simplicity and differentiation accuracy
Solution Approach 2:
The system introduces an intermediary step that detects whether a target object is present in the field of view before initiating detailed identification. This intermediary detection mechanism enables the system to maintain simplicity during normal operation while achieving high precision when similar objects need to be differentiated
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training using an automated system. One of the methods includes determining, using first sensor data obtained by a sensor at a property, that a candidate target object satisfies a similarity threshold for each of two or more predetermined objects; in response to determining that the candidate target object satisfies the similarity threshold, accessing second sensor data of the candidate target object; determining, using at least the second sensor data, that the candidate target object is a target object from the two or more predetermined objects; in response to determining that the candidate target object is the target object, selecting, from a plurality of automated actions each for a corresponding one of the two or more predetermined objects, an automated action; and performing the automated action.