Wearable Sensor-Based Video Event Segmentation
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Solution Overview
Problem
Users face difficulties in efficiently editing and sorting through large amounts of video footage, particularly when trying to identify specific clips of interest, due to the cumbersome nature of reviewing extensive recordings on portable video devices.
Innovation Solution
A platform that utilizes simultaneously recorded non-image-based sensor data to automatically segment and classify events within the footage, allowing users to filter and retrieve specific video clips based on activity metadata and sensor signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review and sort through large amounts of video footage, then they can identify specific clips of interest, but the time and effort required increases linearly with the amount of footage
Solution Approach 1:
The system performs preliminary segmentation and classification of video footage into events using sensor data before the user needs to review it. Event boundaries and metadata are pre-computed and stored, allowing users to quickly search and filter clips without manually watching entire recordings. This advance processing dramatically reduces the time users spend reviewing footage while maintaining accurate clip identification.
Solution Approach 2:
The system introduces sensor data (accelerometer, gyroscope, magnetometer, barometer, GPS) as an intermediary between the raw video footage and the user's search requirements. These sensors capture motion and environmental characteristics that automatically distinguish different events, serving as a mediator that enables automated segmentation and classification without requiring users to manually analyze video content.
2Reliability
If the video recording device captures continuous footage of activities, then all moments are recorded, but it becomes difficult to locate specific events within the footage
Solution Approach 1:
The system segments continuous video footage into discrete events based on sensor data analysis. By detecting changes in motion patterns, orientation, and environmental parameters, the system automatically divides uninterrupted recordings into meaningful event segments with clear boundaries. This segmentation maintains complete event capture while making it easy for users to locate and retrieve specific events through metadata search and filtering.
Solution Approach 2:
The system uses sensor signal characteristics (analogous to color changes) to automatically identify and mark event boundaries. Specific patterns in accelerometer, gyroscope, magnetometer, barometer, and GPS data serve as indicators that distinguish different event types, enabling the system to automatically tag and categorize footage segments for easy retrieval.
3Quantity of substance
If users increase the amount of footage recorded to ensure all moments are captured, then more content is available, but the complexity of managing and sorting the footage increases
Solution Approach 1:
The system enables self-service footage management by automatically segmenting, classifying, and organizing video content based on sensor data without requiring user intervention. The device autonomously creates event boundaries, generates metadata, and structures footage for efficient retrieval, allowing users to manage large volumes of footage without proportionally increasing management complexity.
Solution Approach 2:
The system replaces manual mechanical sorting and review processes with automated electronic classification based on sensor data analysis. Instead of users physically navigating through footage, the system uses digital metadata and sensor-based event detection to organize and retrieve clips, dramatically reducing the complexity of managing large footage volumes.
Data Source
AI summary
In one embodiment, a method includes, by an electronic device, accessing activity data containing one or more non-image-based sensor signals from a first wearable device, where the activity data corresponds to an activity a user performs during a first timeframe, accessing from a first camera device, one or more cameras of the first camera device, where the video data corresponds to the first activity of the first user during the first timeframe, segmenting the activity data based on one or more features of the one or more non-image-based sensor signals to identify one or more segments of activity data corresponding to a second timeframe, classifying the one or more segments of the video data based on the one or more identified events associated with the first activity during the second timeframe, classifying the segments of the video data based on the one or more events during the second timeframe.