Time-Lapse Video Generation via Interest Curve Analysis
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Solution Overview
Problem
Manually changing video playback speeds can be time-consuming and discourages users from experiencing the entire video.
Innovation Solution
A system and method for generating a time-lapse video by extracting images, grouping them based on similarity and sequence, detecting classified visuals, determining image classification weights, and using these weights to create an interest curve and retime curve to select time-lapse images, which are then used to generate a time-lapse video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually change video playback speeds, then they can experience key moments more quickly, but the process becomes time-consuming and discourages users from viewing the entire video
Solution Approach 1:
The system performs preliminary analysis of the video content before playback, extracting key moments and generating a time-lapse video that highlights important segments. This preliminary action eliminates the need for users to manually adjust playback speeds during viewing, as the interesting segments are already identified and optimized in the generated video.
Solution Approach 2:
The system automatically analyzes video content, detects interesting segments using machine learning models, and generates an optimized time-lapse video without user intervention. The video processing system serves itself by autonomously identifying key moments and creating the final output, freeing users from manual speed adjustment tasks.
2Ease of operation
If users manually adjust playback speeds to experience key moments, then they can focus on interesting segments, but the complexity of operation increases and discourages full video viewing
Solution Approach 1:
The system extracts and separates interesting segments from the full video content, creating a condensed time-lapse version that contains only the most relevant moments. This extraction eliminates the need for users to manually navigate through entire videos or adjust playback speeds, as the interesting content is already isolated and presented in an optimized format.
Solution Approach 2:
The system transforms the video by changing temporal parameters, compressing long durations into shorter timeframes while preserving key events. By automatically adjusting the temporal distribution of video content based on detected interest levels, the system simplifies user interaction while maintaining ease of operation.
3Loss of information
If the entire video is viewed at normal speed, then all content is captured, but time-consuming and less engaging for users interested only in key moments
Solution Approach 1:
The system performs preliminary analysis of the video content to identify and mark interesting segments before generating the time-lapse video. This preliminary action ensures that key information is detected and preserved in the condensed version, allowing users to experience essential content without viewing the entire original video.
Solution Approach 2:
Instead of presenting the complete original video, the system applies partial action by selecting and emphasizing only the most interesting segments. This approach provides sufficient video content coverage for user engagement while significantly reducing viewing time, as the system focuses on delivering the most valuable portions of the video.
Data Source
AI summary
Images may be extracted from a video. The images may be grouped into image groups. Numbers and types of classified visuals within the images may be detected. Individual types of classified visuals may correspond to individual classification weights. Image classification weights for the images may be determined based on the numbers and the types of classified visuals and the individual classification weights. Interest weights for the images may be determined based on the image classification weights and the sizes of the image groups to which the individual images belong. An interest curve may be generated based on the interest weights. A retime curve may be generated based on the interest curve. Time lapse images to be included in the time lapse video may be determined based on the images and the retime curve. The time lapse video may be generated based on the time lapse images.