Trail Camera Mapping for Non-Disruptive Animal Movement Prediction
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Solution Overview
Problem
Existing technologies fail to accurately track and predict the movement habits of animals in the wild without disrupting their natural behavior, and there is a lack of devices for detailed time-related habit analysis.
Innovation Solution
A system and method using cellular or WiFi trail cameras and sensors to capture animal movement data, integrate it with weather data, and perform statistical analysis to predict future movements, utilizing a mapping system with a server to process and share data among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trail cameras and sensors are used to track animal movement, then data collection capability is improved, but the system complexity and cost increase significantly
Solution Approach 1:
The patent combines multiple data sources (trail cameras, sensors, weather data, GPS locations) into a unified machine learning model that processes all inputs together to predict animal movement, reducing the need for separate complex tracking systems
Solution Approach 2:
The system uses image processing to analyze photos from trail cameras and extract animal movement patterns, creating a digital representation of physical animal behavior that can be processed without direct physical tracking devices on the animals
2Measurement precision
If environmental factors and historical data are integrated into the prediction model, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores historical animal movement data, environmental data, and weather data in structured formats before prediction is needed, allowing the machine learning model to quickly query and analyze relevant patterns without performing heavy data preparation during prediction events
Solution Approach 2:
The machine learning model dynamically adjusts which environmental parameters and historical data points are weighted most heavily based on the specific prediction context, allowing it to focus computational resources on the most relevant factors rather than processing all data equally
3Measurement precision
If detailed image processing is performed to identify animal species and direction of travel, then data accuracy is improved, but processing time and computational energy increase
Solution Approach 1:
The system performs image processing at different levels of detail based on needs - using full image analysis when species identification is critical, but relying on simpler sensor data when only general movement patterns are needed, avoiding unnecessary computational expenditure
Data Source
AI summary
A mapping system for tracking game within a property. The mapping system includes a plurality of devices and a system of servers. The plurality of devices is configured to provide records of game. Each of the records includes data. The system of servers is configured to receive the records of game from the devices and create an event for each record. Each event is assigned location information that is specific to the device that supplied the record. The system of servers includes a processor configured to predict a likelihood of a game sighting at a specific time and location within the property based on a plurality of events.