Ocean Feature Detection From Camera Images for Faster Beach Assessment
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Solution Overview
Problem
Existing methods for determining ocean conditions, such as surf conditions and recreational activities, from real-time video footage are tedious, time-consuming, and prone to human error, limiting the accuracy and quality of information available to beachgoers planning their visits.
Innovation Solution
A method using machine-learning models trained on annotated image data from ocean-facing cameras to automatically detect features and parameters, such as wave height and surfer activities, and estimate environmental measures, enabling efficient data generation and user alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of ocean conditions from video footage is used, then information gathering is possible, but the process is tedious, time-consuming, and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated computer vision system. Machine learning models analyze video footage from ocean-facing cameras to automatically detect wave characteristics, surf conditions, and recreational activities, eliminating the need for human observers to manually evaluate conditions while improving both accuracy and speed.
Solution Approach 2:
The system enables self-service by allowing the ocean condition assessment to perform itself through automated image analysis. The machine learning models independently process video data, identify features, and generate condition reports without human intervention, making the system autonomous and eliminating time loss associated with manual assessment.
2Loss of information
If human observers study video footage to assess ocean conditions, then some information can be gathered, but the amount and quality of information is severely limited
Solution Approach 1:
The automated detection system performs multiple functions simultaneously: it detects wave height, wave frequency, ocean currents, wind conditions, and recreational activities from the same video footage. This multi-functional approach comprehensively captures all relevant ocean conditions without requiring separate systems for each parameter, thus minimizing information loss while managing complexity through integrated processing.
Solution Approach 2:
The system transforms visual information from video frames into quantitative parameters through machine learning models. By converting image data into measurable metrics such as wave height in meters, current speed in knots, and wind direction in degrees, the system comprehensively captures ocean conditions in a structured format that is both complete and easily analyzable.
3Productivity
If real-time video footage from ocean-facing cameras is used, then beach condition monitoring is possible, but manual analysis requires watching ten or more minutes of footage for each beach
Solution Approach 1:
The patent replaces manual video review with automated machine learning-based image analysis. The system processes video footage from multiple ocean-facing cameras simultaneously, extracting ocean condition data instantaneously without requiring human observers to watch extensive footage, thereby dramatically increasing productivity and reducing time loss.
Solution Approach 2:
The system performs preliminary analysis by continuously processing video footage in real-time and pre-computing ocean condition metrics. When users request beach condition information, the data is already analyzed and ready for immediate delivery, eliminating the need for users to spend time watching footage and enabling rapid assessment of multiple beaches.
Data Source
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AI summary
Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.