AI Image Data Curation for Searchable Home Security Footage
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Solution Overview
Problem
Existing home security and automation systems struggle to efficiently handle and present relevant image data, making it challenging to locate and review desired or interesting footage due to the vast amount of unfiltered video footage, which often results in a decrease in actual review likelihood.
Innovation Solution
Implementing AI models in cameras to process image and audio data, combined with radar sensors, to detect and identify objects and events, and generate tagged image data for easier searching and summarization of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video footage is stored for later viewing, then the amount of available footage increases, but the challenge of reviewing and locating desired portions increases
Solution Approach 1:
The system performs preliminary actions by automatically generating summaries, detecting entities and events, and creating searchable tags during or immediately after footage capture. This pre-processing organizes the vast amount of video data into structured, easily navigable formats before the user needs to review it, eliminating the need to manually search through hours of footage.
Solution Approach 2:
The system introduces an intermediary layer between the raw video footage and the user interface. This intermediary automatically processes footage to extract key information (entities, events, timestamps) and presents it in summarized forms such as timelines, thumbnails, or structured data, allowing users to navigate and locate desired portions without reviewing every frame.
2Reliability
If more video footage is captured to ensure comprehensive monitoring, then coverage improves, but the time required to review footage increases
Solution Approach 1:
The system extracts only the most relevant portions of comprehensive video footage for user review. By automatically detecting entities (people, animals, vehicles), events (falling, running, approaching), and generating descriptive tags, the system separates key information from irrelevant content, presenting only the extracted meaningful segments to users while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The system changes parameters by transforming raw video data into structured information with multiple attributes (entity types, event types, timestamps, locations, confidence scores). This parameter transformation enables efficient filtering and searching, allowing users to quickly locate specific events without reviewing entire footage sets, thus reducing review time while maintaining comprehensive coverage.
3Ease of operation
If AI models are implemented to process and tag image data, then the ability to search and filter improves, but the device complexity increases
Solution Approach 1:
The system implements self-service by automatically processing, tagging, and organizing video footage without requiring manual intervention. AI models autonomously detect entities and events, generate descriptive tags, and structure data for searching and filtering. This automation eliminates the need for manual footage review and organization, providing enhanced search capabilities while managing complexity through automated processes.
Data Source
AI summary
Presented herein are system and methods for handling image data of home security and/or automation applications. Portions of image data (e.g., clips) can be tagged for later searching and/or presenting. A summary of events can be presented, according to tagged image data.