Video Tagging via Circular Buffering and Trigger Detection
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Solution Overview
Problem
Electronic devices capture large volumes of video data, making it difficult to identify and isolate moments of interest, as users often miss capturing significant events due to being unprepared.
Innovation Solution
The system captures and buffers video data, allowing users to tag moments of interest in real-time or shortly after, enabling the separation of key events from the larger dataset through circular buffering and server-based video summarization using tags generated by devices or smartphones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is captured over a lengthy period of time, then the volume of captured moments increases, but it becomes difficult to identify and isolate moments of interest
Solution Approach 1:
The system performs preliminary tagging during or immediately after video capture by detecting triggers (motion, audio keywords, facial expressions) and automatically assigning tags to video segments. This preliminary organization of video data by tags enables efficient retrieval and isolation of moments of interest without requiring manual review of entire lengthy video recordings
Solution Approach 2:
Tags serve as an intermediary layer between the large volume of video data and the user's need to identify specific moments. Instead of directly searching through video content, users interact with tags that mediate the selection process, making it easier to isolate relevant segments from extensive video recordings
2Measurement precision
If users manually review video data to identify moments of interest, then accurate identification is possible, but extensive time is required
Solution Approach 1:
The system performs automatic trigger detection and tag generation without requiring continuous user intervention. The device autonomously monitors video and audio inputs, detects predefined triggers (motion thresholds, audio keywords, facial expressions), and automatically tags video segments, eliminating the need for users to manually review entire video datasets while maintaining accurate identification of moments of interest
Solution Approach 2:
The system provides feedback to users through generated tags that indicate detected moments of interest. Users can review these automatically generated tags and make adjustments if needed, creating a feedback loop that combines automated detection accuracy with user verification efficiency
Data Source
AI summary
Devices, systems and methods are disclosed for allowing multiple devices to generate tags associated with video data captured by a single device. For example, a recording device may capture and upload video data and the multiple devices may generate tags identifying memorable moments. The tags may be used to generate a video summary, such as a single video summary for the captured video data that includes video data associated with tags generated by multiple devices. In addition, the tags may be associated with unique devices and may be used to generate multiple video summaries. For example, the tags may be used to generate individual video summaries for each of the multiple devices. Therefore, multiple devices may tag the captured video data to generate the single video summary and/or multiple individual video summaries.