Method and home appliance device for generating time-lapse video
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Solution Overview
Problem
Existing home appliances, such as ovens, struggle to automatically generate time-lapse videos of cooking processes due to limited storage space, processing resources, and connectivity, and cannot efficiently select and store images to create a cohesive video.
Innovation Solution
A method and device that compare obtained images with stored images to determine changes, generating a time-lapse video by selecting and storing images based on time differences and feature values, and applying special effects, while optimizing storage and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all captured images are stored in memory, then the time-lapse video can be comprehensive and detailed, but the storage space is quickly exhausted in low-resource environments
Solution Approach 1:
The system performs preliminary comparison and selection of images during the cooking process itself, identifying and storing only key frames that represent significant changes in the cooking process. This preliminary action ensures that when video generation occurs, the necessary images are already selected and stored, eliminating the need to store all captured images.
Solution Approach 2:
The system changes the parameter of image selection from storing all images to storing only images that meet specific criteria (time difference threshold and feature value threshold). This parameter-based filtering allows the system to maintain video quality while significantly reducing storage requirements by storing only essential frames.
2Manufacturing precision
If images are selected based on detailed comparison and multiple criteria, then the video quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The system calculates and compares feature values and time differences for each captured image in real-time during the cooking process. This preliminary processing identifies key frames ahead of time, so that when video generation is requested, the selection work is already complete, reducing the processing time during video creation.
Solution Approach 2:
Instead of performing complex video editing and frame selection when the video is requested, the system creates a simplified representation (a selection of key frames with their metadata) during the cooking process. This copying of essential information allows for rapid video generation later without re-processing the original images.
3Loss of information
If the system stores and processes more images to create a detailed time-lapse video, then the video comprehensiveness is improved, but the device complexity and resource requirements increase
Solution Approach 1:
The system introduces two key parameters for image selection: time difference (how much time has passed since the last stored image) and feature value (how much the image content has changed). By changing from storing all images to storing images that meet these parameter thresholds, the system maintains cooking process detail while dramatically reducing storage and processing requirements.
Solution Approach 2:
The cooking process is segmented into key moments represented by selected images, rather than treating it as a continuous stream of all captured images. This segmentation identifies and stores only the essential frames that represent significant cooking stages, reducing resource requirements while preserving the narrative of the cooking process.
4Speed
If images are captured at frequent intervals, then the time-lapse video shows smooth progression, but the storage space is exhausted quickly
Solution Approach 1:
The system uses a time difference parameter to dynamically determine which captured images to store. Images are stored when the time difference from the previously stored image exceeds a threshold, allowing frequent capture for smooth video progression while storing only a subset of images to conserve storage capacity.
Solution Approach 2:
The system captures images at frequent intervals (excessive action) to ensure smooth video progression, but stores only a partial subset of these images based on comparison with previously stored images. This partial storage approach maintains video quality while avoiding the storage exhaustion that would result from storing all captured images.
Data Source
AI summary
A method of generating a time-lapse video includes: identifying an image storage mode; obtaining a first image; based on identifying that the image storage mode is an emphasis mode for emphasizing one or more images, obtaining a first time difference between the first image and a stored image, and a first feature value indicating a first amount of change between the first image and the stored image; for each respective image of a first plurality of images of a first image group stored in a memory, identifying a second image from among the first plurality of images, based on a second time difference and a second feature value; generating a second image group by removing the second image from the first image group and adding the first image to the first image group; and generating the time-lapse video by using a second plurality of images of the second image group.