Security Camera Weather Classification for Property-Level Forecasting
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Solution Overview
Problem
Current weather prediction methods often provide inaccurate or unreliable data at a geographic scale that is too broad or long-term, making it difficult to determine the precise weather conditions at specific locations, such as within a zip code, which can lead to ineffective property protection from weather-related damage.
Innovation Solution
A system utilizing video classification capabilities that trains a neural network model with recorded media from security cameras and sensor data to accurately classify local weather patterns, allowing for real-time monitoring and automation of security measures, such as closing doors and windows, based on localized weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather prediction is aggregated at a broad geographic level, then data coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the broad geographic area into multiple localized zones, each with its own weather classification. Instead of providing a single aggregated weather forecast for a large region, the system divides the area into smaller segments (e.g., different neighborhoods or property-level zones) and applies machine learning models to classify weather conditions independently for each segment. This allows the system to maintain broad geographic coverage while achieving high measurement precision at the local level.
2Reliability
If traditional weather forecasting methods are used, then system complexity is reduced, but reliability deteriorates
Solution Approach 1:
The patent replaces traditional mechanical weather forecasting systems (physical weather stations, manual observation methods) with machine learning-based classification models. These models process visual data from cameras and sensor data to automatically classify weather conditions. This substitution significantly improves reliability by providing more accurate, localized weather information, while the complexity is managed through automated processing and integration with existing security camera infrastructure.
3Measurement precision
If security camera footage is used for weather classification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes security cameras multi-functional by enabling them to serve both their original security monitoring purpose and new weather classification function. The same camera footage used for security purposes is also processed by machine learning models to detect weather conditions. This universality approach improves measurement precision for localized weather detection without requiring additional dedicated weather monitoring devices, thereby limiting the increase in device complexity.
Data Source
AI summary
Systems and techniques are described for utilizing video classification capabilities for providing accurate local weather. In some implementations, the techniques include the actions of obtaining images from cameras located at a monitored property. An expected weather forecast and an actual weather condition is obtained for the monitored property. A machine-learning model is trained to classify a current weather condition for the monitored property using the images from the cameras, the expected weather forecast, and the actual weather condition. A weather condition is obtained from the trained machine-learning model that indicates a particular weather condition at the monitored property based on one or more images from a camera and the expected local weather forecast at the monitored property.