Camera System Region Activity Filtering
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Solution Overview
Problem
Existing camera systems struggle to differentiate between relevant and irrelevant activity within their field of view, leading to unnecessary notifications for users, especially in high-traffic areas.
Innovation Solution
A camera system that identifies high-frequency activity regions within its field of view, allows users to tag regions of interest, and uses neural networks to automatically detect and notify users of specific events in those regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a camera captures all activity within its field of view, then comprehensive monitoring is achieved, but excessive notifications are generated for irrelevant activity
Solution Approach 1:
The field of view is divided into multiple regions of interest (ROIs), each independently monitored and tagged. The system segments the monitoring space into zones such as front yard, driveway, and street, allowing selective notification based on which specific region contains the activity rather than notifying for all activity uniformly across the entire field of view.
Solution Approach 2:
Different regions within the field of view are assigned different importance levels and notification priorities. The system applies local quality by allowing users to tag specific regions as more or less important, so that activity in high-priority regions triggers notifications while activity in low-priority regions does not, creating differentiated monitoring quality across different spatial locations.
2Loss of information
If a camera notifies users of all detected activity, then complete awareness is provided, but user experience deteriorates due to irrelevant notifications
Solution Approach 1:
Users perform preliminary tagging of regions of interest before monitoring begins. The system allows users to pre-define which regions are important to monitor and tag them accordingly, so that the notification filter is already in place before any activity occurs, preventing irrelevant notifications from being generated in the first place rather than filtering them afterward.
Solution Approach 2:
The system provides feedback to users about which regions have been tagged and how monitoring is performing. Users can review the tagged regions and adjust their preferences based on actual usage patterns, creating a feedback loop that continuously improves the relevance of notifications and user experience over time.
3Measurement precision
If region detection is performed manually, then accuracy is high, but system complexity increases
Solution Approach 1:
The system performs automatic region detection and tagging without requiring manual user input. The neural network automatically analyzes the field of view, identifies distinct regions, and tags them with descriptive labels, making the system self-sufficient in the region detection task that would otherwise require manual configuration.
Solution Approach 2:
The patent replaces manual mechanical region detection with an automated neural network-based computer vision system. Instead of requiring users to manually draw and tag regions, the system uses machine learning algorithms to automatically detect and classify regions, substituting human manual work with automated computational processing.
Data Source
AI summary
A camera system may automatically identify high frequency motion regions in a field of view of the camera system. Analysis of motion events detected at different regions of a scene in the field of view, may be performed. Similar events frequently occurring at a certain region may be recorded/accumulated and compared to a threshold. Regions with high frequency activity may be determined and a user may be notified. If the user is not interested in events within the high activity regions, the regions may be ignored and notifications to the user may cease. The camera system may determine specific actions/events occurring in a region, such as a person approaching or leaving a home.