Trail Camera Analytics for Animal Activity Forecasting
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Solution Overview
Problem
Existing trail camera systems lack sophisticated analytics capabilities, burdening users with the cumbersome task of interpreting large volumes of raw image data and metadata to extract useful insights about animal activity.
Innovation Solution
A trail camera system that implements multiple analytics models, including Poisson, ordinary least squares, and negative binomial distribution models, to aggregate data from multiple cameras, determine peak activity times, and forecast animal activity based on contemporaneous and forecasted weather data, while cleansing duplicative data through object detection and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple trail cameras are deployed to capture comprehensive animal activity data, then the quantity and coverage of animal activity data is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent combines data from multiple trail cameras into a unified database, merging image data, metadata, and analytics results from various camera locations into a single centralized system that can be processed collectively rather than individually
Solution Approach 2:
The patent introduces an intermediary analytics system that automatically processes camera data through multiple analytical models (Poisson distribution, ordinary least squares, negative binomial distribution) to extract insights without requiring user intervention in the complex processing steps
2Loss of information
If digital trail cameras with additional metadata are used to capture more information, then the information content of the data is improved, but the burden of processing and interpreting the data increases
Solution Approach 1:
The patent implements self-service automation where the system automatically performs data cleansing, validates metadata, applies analytical models, and generates forecasts without requiring user intervention in the complex processing steps
Solution Approach 2:
The patent replaces manual data processing and interpretation with automated computational models including Poisson distribution for event counting, ordinary least squares for trend analysis, and negative binomial distribution for forecast generation
3Device complexity
If manual analysis methods are used to interpret trail camera data, then the simplicity of the system is improved, but the productivity of extracting useful insights deteriorates
Solution Approach 1:
The patent performs preliminary data cleansing and validation automatically before analysis, pre-processing the raw camera data by removing duplicates, validating metadata completeness, and organizing data structures in advance of the analytical modeling phase
Solution Approach 2:
The patent transforms raw camera data into meaningful insights by applying parameter-based analytical models that convert image counts, timestamps, and metadata into forecasted animal activity patterns with statistical confidence intervals
Data Source
AI summary
A system, and method thereof, is provided for identifying and forecasting animal activity. The method may comprise receiving the images from the camera to define image specific raw data; assimilating the image specific raw data with contemporaneous weather data; determining a mean rate of animal sightings for each of a plurality of prior time intervals associated with the assimilated image specific raw data using a first analytics model; minimizing a difference between observed sightings and predicted sightings based at least in part on outputs from the first analytics model using a second analytics model; receiving forecasted weather data for a plurality of future time intervals; and determining an amount of likely animal sightings for each of the plurality of future time intervals based at least in part on the outputs from the first analytics model and outputs from the second analytics model using a third analytics model.