Photo Diary Video Frame Selection and Organization
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Solution Overview
Problem
Short videos captured on cell phones and IP cameras often go unused, lacking effective methods to utilize and archive them in a meaningful way.
Innovation Solution
A method to create a photo diary by selecting and categorizing candidate frames from digital videos using people and pet detection, face recognition, and scene analysis, presenting representative frames in a calendar, album, timeline, or collage format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video frames are selected and organized using AI detection and recognition technologies, then the photo diary becomes more organized and meaningful, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing videos to extract candidate frames and pre-computing detection features. This preparation work is done in advance before the actual photo diary creation, reducing the processing time needed during final generation while still achieving comprehensive video content utilization.
Solution Approach 2:
The system applies partial action by selectively processing only certain frames from videos rather than analyzing every frame in detail. Candidate frames are identified and extracted first, then only these selected frames undergo full AI detection and recognition processing, balancing thorough content utilization with reasonable processing time.
2Measurement precision
If multiple AI processing steps are applied to video frames, then the quality and accuracy of photo diary entries improve, but the device complexity increases
Solution Approach 1:
The complex processing system is segmented into distinct functional modules: candidate frame extraction, people/pet detection module, face recognition module, and scene analysis module. Each module performs a specific task with defined inputs and outputs, making the overall complex system more manageable and maintainable while preserving high frame selection accuracy.
Solution Approach 2:
The system introduces intermediary components between different processing stages, such as a candidate frame selection layer that filters videos before detailed AI analysis. This intermediary step simplifies the workload for subsequent complex AI processes while maintaining high accuracy in the final photo diary entries.
3Adaptability or versatility
If comprehensive video analysis is performed to create meaningful photo diaries, then the value and usability of archived content increases, but the computational resources required increase
Solution Approach 1:
The system applies local quality by performing different levels of analysis on different portions of video content. High-level scene analysis is applied to all candidate frames, while more computationally intensive face recognition and detailed object detection are applied only to frames where these features are relevant, optimizing energy usage while maintaining versatile photo diary functionality.
Data Source
AI summary
A method to create a photo diary includes creating an entry for a time period in the photo diary. Creating the entry includes selecting candidate frames from digital videos created or received in the time period. Selecting the candidate frames includes performing people and pet detection on the digital videos, extract frames with people and pets from the digital videos perform people recognition on the frames with people to identify frames with recognized persons. The method further includes sorting the candidate frames with recognized persons and pets into groups based on criteria, selecting representative frames from the groups, and presenting the representative frames in the entry.