Security System State Monitoring via Multi-Image ML Analysis
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Solution Overview
Problem
Current security systems face challenges in accurately determining the state of a property or area due to inadequate agreement between image analyses, leading to potential false positives and negatives, which can compromise safety and user experience.
Innovation Solution
A security system that utilizes machine learning models to analyze multiple images from cameras, calculates similarity scores, and requests additional data or user input to confirm states, ensuring accurate state determination by comparing confidence scores and similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single image is used to determine the state of a property, then the system operation is simple and fast, but the measurement precision and reliability are insufficient leading to false positives and negatives
Solution Approach 1:
The system segments the state determination process by analyzing multiple individual images separately through machine learning models, then comparing the results. Each image is evaluated independently to determine a state, and only when there is adequate agreement between multiple image analyses does the system confirm the state, thereby improving accuracy without requiring complete system redesign
Solution Approach 2:
The system merges multiple image analyses together by collecting a subset of images from one or more cameras, analyzing each image to determine a state, and then evaluating the agreement between these states. This combination of multiple independent analyses improves measurement precision while maintaining manageable system complexity through automated comparison processes
2Reliability
If multiple images are analyzed to improve state determination accuracy, then the reliability improves, but the loss of time increases due to additional processing
Solution Approach 1:
The system performs preliminary actions by collecting a subset of images before analysis begins. By having images ready and pre-processed, the system can quickly analyze multiple images in parallel through machine learning models, reducing the overall time loss while maintaining high reliability through multiple corroborating analyses
Solution Approach 2:
The system implements periodic action by continuously monitoring and analyzing images at defined intervals. Rather than analyzing all possible images continuously, the system periodically collects and analyzes subsets of images, which maintains reliable state determination while minimizing time loss through efficient sampling and periodic updates
3Measurement precision
If the system requests additional data or user input to confirm states, then the measurement precision improves, but the ease of operation decreases
Solution Approach 1:
The system implements feedback by analyzing multiple images, determining states for each image, and then evaluating whether there is adequate agreement between these states. When agreement is insufficient, the system automatically requests additional data or user input to confirm the current state, and uses this feedback to improve future determinations. This automated feedback loop improves measurement precision while minimizing user interaction complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for state and event monitoring. In some implementations, images captured by a camera are obtained, the images depicting an area of a property. Two or more images of the images are provided to a machine learning model. An output of the machine learning model is obtained, the output corresponding to the two or more images. One or more potential states of the area of the property are determined using the output of the machine learning model, each state of the one or more potential states corresponding to an image in the two or more images. An action is performed based on the one or more potential states.